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ACT01 - Tatonka Challenge Another quirky Amazon tradition continues—the fifth annual AWS re:Invent Tatonka Challenge. Participation is first come, first serve; reserve seating will not apply. While you won’t begin eating until 10:40 PM, you can queue as early as 8 PM at Titian 2204 in the Venetian, Level 2.   Activity
ACT010 - Bingo Night B-I-N-G-O and Bingo was the game oh!!  If you're not interested in "crawling" to the various pubs, come join us for a fun evening of Bingo. You can even win cool prizes. Limited capacity. Sign up now! Activity
ACT02 - Midnight Madness Kick off re:Invent 2018 the right way by joining Midnight Madness on Sunday, Nov. 25, at 10 PM at The Venetian, Level 2, Hall A. You’ll learn what to expect from this year’s re:Invent, and you'll hear a new service announcement.   Activity
ACT04 - Giving Back: BackPack for Kids Join AWS and VMware in supporting Three Square’s BackPack for Kids program at re:Invent. The BackPack for Kids program provides bags of nutritious, single-serving, ready-to-eat food items each Friday to children who might otherwise go without during weekends and long breaks from school. Come by the Venetian Sands foyer to get involved and help put together a backpack or two. Activity
ACT07 - Hands-On Lego Activities (Monday) Join us in The Quad at ARIA for some fun LEGO activities. Stop by and participate. No preregistration is required. Activity
ACT11 - Hands-On LEGO Activities (Tuesday) Join us in The Quad at ARIA for some fun LEGO activities. Stop by and participate. No preregistration is required. Activity
ACT12 - Hands-On LEGO Activities (Wednesday) Join us in The Quad at ARIA for some fun LEGO activities. Stop by and participate. No preregistration is required. Activity
ACT13 - Hands-On LEGO Activities (Thursday) Join us in The Quad at ARIA for some fun LEGO activities. Stop by and participate. No preregistration is required. Activity
ACT14 - Table Tennis Tournament #1 The re:Invent Table Tennis Tournament is held on Wednesday and Thursday in Hall C. The tournament includes eight tables, and it’s judged by official table tennis referees. Come and watch, or play and compete for a prize. Prizes are awarded to the top four players each day. The tournament is held on both days, so you can choose the day that best fits your schedule. Activity
ACT15 - Table Tennis Tournament #2 The re:Invent Table Tennis Tournament is held on Wednesday and Thursday in Hall C. The tournament includes eight tables, and it’s judged by official table tennis referees. Come and watch, or play and compete for a prize. Prizes are awarded to the top four players each day. The tournament is held on both days, so you can choose the day that best fits your schedule. Activity
ADT201-L - Leadership Session: Digital Advertising - Customer Learning & the Road Ahead In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack. Session Karl Bunch
Alex Collmer
Patrick Wentling
Dave Pickles
ADT201-LOFA - [OVERFLOW] Leadership Session: Digital Advertising - Customer Learning & the Road Ahead (White-Aria) In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack. Overflow
ADT201-LOFB - [OVERFLOW] Leadership Session: Digital Advertising - Customer Learning & the Road Ahead (White-Bellagio) In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack. Overflow
ADT201-LOFM - [OVERFLOW] Leadership Session: Digital Advertising - Customer Learning & the Road Ahead (White-Mirage) In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack. Overflow
ADT201-LOFMGM - [OVERFLOW] Leadership Session: Digital Advertising - Customer Learning & the Road Ahead (White-MGM) In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack. Overflow
ADT201-LOFV - [OVERFLOW] Leadership Session: Digital Advertising - Customer Learning & the Road Ahead (White-Venetian) In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack. Overflow
ADT202 - Use Amazon Rekognition to Power Video Creative Asset Production In this session, hear from an AWS customer about how they leveraged Amazon Rekognition deep learning-based image and video analysis to power a data-driven decision system for creative asset production. Learn how this customer was able to leverage the raw data provided by Amazon Rekognition combined with performance data to discover actionable insights. See a demonstration of the solution, and hear about media- and advertising-specific use cases. Learn from the customer's experiences implementing their architecture, the challenges, and the pleasant surprises along the way. Session Karl Bunch
James Kupernik
Joline McGoldrick
ADT205 - Go Global with Cloud-Native Architecture: Deploy AdTech Services Across Four Continents Plista, a Germany-based advertising solution provider, discusses how they use a cloud-native architecture, container-first approach to speed up development, increase agility, reduce latency, localize storage, and foster innovation and ownership in their organization. They demonstrate how a cloud blueprint is used to easily roll out their services to new global markets. With this architecture, Plista processes 1.7 billion requests per day, across four continents. They also discuss how they're adapting for GDPR compliance and redesigning parts of their platform to leverage new AWS services. Chalk Talk Torben Brodt
Philipp Hoffman
ADT301 - Create a Serverless Web Event Pipeline Collecting events at web scale can be daunting, frustrating, and a distraction from building technology that adds value to your company's core offerings for your customers. In this session, learn how to design and deploy serverless pipelines for collecting events at web scale, such as advertising pixels, site visits, site interaction, and e-commerce. We work together to build a serverless collection pipeline utilizing Amazon Route 53, Amazon CloudFront, AWS Lambda@Edge, Amazon Kinesis Data Firehose, Elasticsearch, Amazon S3, AWS Glue, and Amazon Redshift. At the end of this session, you will understand ways you can deploy a serverless pipeline in your architectures. Builders Session Karl Bunch
ADT302 - Democratize Data Preparation for Analytics & Machine Learning: A Hands-On Lab Machine learning (ML) outcomes are only as good as the data they are built upon. Preparing data for ML is time consuming and cumbersome; “data wrangling” for analytics can consume over 80% of project effort. ML Wrangling Assistant, based on Trifacta running on AWS, streamlines ML applications so teams can focus on the work that matters—creating accurate predictions that improve products, services, and organizational efficiency. In this lab, we cover one of two data preparation use cases. Marketing Analytics analyzes web ads by cleaning and transforming ecommerce transactions in a relational table combined to a clickstream semi-structured log file. Cross-Sell Analytics explores, structures, standardizes, and combines multiple file types (CSV, JSON, Excel) to create a single, consistent view of customers. Final outputs are the categorical features and attributes to train, test, and validate the data sets required by Amazon SageMaker to perform ML modeling. Builders Session Vijay Balasubramaniam
Jose Noriega
ADT303 - ML for Real-Time Self-Service Trend Detection & Root Cause Analysis: A Hands-On Lab Business intelligence (BI) has been a manual, static process. Analysts and engineers must anticipate the right questions, monitor the right metrics, create dashboards, apply calculations, verify and validate results, and repeat the process—which means slow, reactive insights and dangerous blind spots across business operations. In this session, get hands-on with a range of datasets across use cases like product and customer experience analytics from ecommerce, web analytics, telco, ad tech, gaming, ride sharing, and IoT manufacturing. Learn how a new class of self-learning AI/ML tools is enabling autonomous analytics with trend and root cause detection services in real time. Anodot's SaaS analytics platform offers a simple self-service model with no data science expertise required, powered by AWS, Amazon Kinesis, and Amazon Redshift. Builders Session Uri Maoz
Jose Noriega
ADT401 - Real-Time Web Analytics with Amazon Kinesis Data Analytics Knowing what users are doing on your websites in real time provides insights you can act on without waiting for delayed batch processing of clickstream data. Watching the immediate impact on user behavior after new releases, detecting and responding to anomalies, situational awareness, and evaluating trends are all benefits of real-time website analytics. In this workshop, we build a cost-optimized platform to capture web beacon traffic, analyze it for interesting metrics, and display it on a customized dashboard. We start by deploying the Web Analytics Solution Accelerator, then once the core is complete, we extend their solution to capture new and interesting metrics, process those with Amazon Kinesis Analytics, and display new graphs on their custom dashboard. Participants come away with a fully functional system for capturing, analyzing, and displaying valuable website metrics in real time. Workshop Chris Marshall
Luke Youngblood
Mark LaRosa
AFT01 - Camp re:Invent Trivia Challenge Relive your summer camp days while playing a trivia game at Camp re:Invent. This is not just any trivia game. Join us for the Camp re:Invent Trivia Challenge, and compete against AWS VP, Jeff Barr. After Hours
AFT02 - Board Game Night and Tweetup Come join us for Board Game Night and Tweetup with the Social Media team. Play games and post to social media about your experiences during the Tweetup. After Hours
AIM01 - [NEW LAUNCH!] AWS DeepRacer – The MGM Speedway (Wednesday) Come and race AWS DeepRacer for prizes and glory at the AWS DeepRacer MGM Speedway. We have 6 race tracks in the arena and 2 in the hacker garage for you to come and get hand-on with reinforcement learning. Bring your own model created in the AWS DeepRacer workshop or use one of ours to see AWS DeepRacer in action on the tracks, and a chance to enter the AWS DeepRacer League leaderboard. The top spots on the leaderboard at 10:30pm Wednesday 29th November will advance to the final race held at 8am on Thursday morning in Keynote Hall A. Finalists will compete to be crowned the AWS DeepRacer re:Invent League 2018 Champion and to emerge victorious with the AWS DeepRacer League Cup. Activity
AIM02 - [NEW LAUNCH!] AWS DeepRacer – The MGM Speedway (Thursday) Come and race AWS DeepRacer for prizes and glory at the AWS DeepRacer MGM Speedway. We have 6 race tracks in the arena and 2 in the hacker garage for you to come and get hand-on with reinforcement learning. Bring your own model created in the AWS DeepRacer workshop or use one of ours to see AWS DeepRacer in action on the tracks, and a chance to enter the AWS DeepRacer League leaderboard. The top spots on the leaderboard at 10:30pm Wednesday 29th November will advance to the final race held at 8am on Thursday morning in Keynote Hall A. Finalists will compete to be crowned the AWS DeepRacer re:Invent League 2018 Champion and to emerge victorious with the AWS DeepRacer League Cup. Activity
AIM03 - [NEW LAUNCH!] AWS DeepRacer League 2018 Championship Cup Final The race in on! Join us to watch the live action at the re:Invent International Speedway where three customers will compete to be crowned the AWS DeepRacer re:Invent League 2018 Champion. Customers will be selected for the final based on their time trial results from the AWS DeepRacer MGM Speedway races. You need to have one of the top spots on the leaderboard between 11:30am-10:30pm Wednesday 28th November to be in the running for a place in the final and to emerge victorious with the AWS DeepRacer League Cup. Activity
AIM202-L - Leadership Session: Machine Learning Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Session Swami Sivasubramanian
Ratna Saripalli
AIM202-LOFA - [OVERFLOW] Leadership Session: Machine Learning (Teal-Aria) Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Overflow
AIM202-LOFB - [OVERFLOW] Leadership Session: Machine Learning (Teal-Bellagio) Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Overflow
AIM202-LOFM - [OVERFLOW] Leadership Session: Machine Learning (Teal-Mirage) Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Overflow
AIM202-LOFMGM - [OVERFLOW] Leadership Session: Machine Learning (Teal-MGM) Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Overflow
AIM202-LOFN - [OVERFLOW] Leadership Session: Machine Learning (3-Nuvola) Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Overflow
AIM202-LOFS - [OVERFLOW] Leadership Session: Machine Learning (3-Scamall) Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Overflow
AIM202-LOFV - [OVERFLOW] Leadership Session: Machine Learning (Teal-Venetian) Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI. Overflow
AIM203-S - Patient-Focused Data Science: Machine Learning for Complex Diseases Curious about how Amazon machine learning (ML) services can enable healthcare organizations to find the insights they need to survive and thrive? Join us to learn how Takeda researchers built and trained their own disease-specific ML models, including deep-learning models using Deloitte ConvergeHEALTH running on AWS to simulate and quantify the overall disease burden and identify potential risks. This session is brought to you by AWS partner, Deloitte Consulting LLP. Session Dan Housman
Jennifer Drahos
Valerie Strezsak
AIM204-S - Smarter Event-Driven Edge with Amazon SageMaker & Project Flogo A single device can produce thousands of events every second. In traditional implementations, all data is transmitted back to a server or gateway for scoring by a machine learning (ML) model. This data is also stored in a data repository for later use by data scientists. In this session, we explore data science techniques for dealing with time series data leveraging Amazon SageMaker. We also look at modeling applications using deterministic rules with streaming pipelines for data prep, and model inferencing using deep learning frameworks directly onto edge devices or onto AWS Lambda using Project Flogo, an open-source event-driven framework. This session is brought to you by AWS partner, TIBCO Software Inc. Session Matt Ellis
Abram Van Der Geest
AIM205 - New AI/ML Solutions with AWS DeepLens & Amazon SageMaker with ConocoPhillips ConocoPhillips is exploring the combination of machine vision and machine learning. Four proof of concepts were developed using AWS DeepLens, Amazon SageMaker, Amazon S3, and more. These projects address the security, safety, and inventory associated with upstream field operations. In this session, we describe our successes, challenges, and lessons learned. We also share our ideas for future product improvements. Chalk Talk Mahendra Bairagi
Karla Wasinger
Corey Vessar
Blake Kobel
AIM206-R1 - [NEW LAUNCH!] [REPEAT 1] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Kristian Alexander
Sunil Mallya
Mike Miller
AIM206-R10 - [NEW LAUNCH!] [REPEAT 10] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Donnie Prakoso
AIM206-R11 - [NEW LAUNCH!] [REPEAT 11] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Justin De Castri
AIM206-R12 - [NEW LAUNCH!] [REPEAT 12] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Brian Townsend
AIM206-R13 - [NEW LAUNCH!] [REPEAT 13] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Sunil Mallya
AIM206-R14 - [NEW LAUNCH!] [REPEAT 14] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Brian Townsend
AIM206-R15 - [NEW LAUNCH!] [REPEAT 15] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Bradley Kenstler
AIM206-R16 - [NEW LAUNCH!] [REPEAT 16] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Start your engines, the race is on! Workshop Justin De Castri
AIM206-R17 - [NEW LAUNCH!] [REPEAT 17] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Sunil Mallya
AIM206-R18 - [NEW LAUNCH!] [REPEAT 18] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Todd Escalona
AIM206-R19 - [NEW LAUNCH!] [REPEAT 19] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Brian Townsend
AIM206-R2 - [NEW LAUNCH!] [REPEAT 2] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Jason Holgerson
AIM206-R20 - [NEW LAUNCH!] [REPEAT 20] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Bradley Kenstler
AIM206-R3 - [NEW LAUNCH!] [REPEAT 3] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Todd Escalona
AIM206-R4 - [NEW LAUNCH!] [REPEAT 4] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Jason Holgerson
AIM206-R5 - [NEW LAUNCH!] [REPEAT 5] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Start your engines, the race is on! Workshop Todd Escalona
AIM206-R6 - [NEW LAUNCH!] [REPEAT 6] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Muhyun Kim
AIM206-R7 - [NEW LAUNCH!] [REPEAT 7] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop DeClercq Wentzel
AIM206-R8 - [NEW LAUNCH!] [REPEAT 8] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Muhyun Kim
AIM206-R9 - [NEW LAUNCH!] [REPEAT 9] AWS DeepRacer Workshops –a new, fun way to learn reinforcement learning Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on! Workshop Justin De Castri
AIM207-S - Faster, Better, Cheaper: AI Apps in One-Tenth the Time and Cost In this session, learn how the C3 Platform on AWS is architected and why it accelerates the development of enterprise-scale AI applications. Hear how customers like the US Air Force, Enel, and global manufacturing leaders are using C3 on AWS to rapidly aggregate, unify, federate, and normalize data from sensor networks and enterprise IT systems, and apply ML/AI algorithms against this data to unlock significant economic value. Hear from global organizations that are solving complex business challenges, from optimizing the supply network, to predicting which assets will fail, to identifying fraud and money laundering. This session is brought to you by AWS partner, C3. Session Dib Banerjee
Jim Hassman
AIM208-S - Accelerating Enterprise-Scale AI Application Development In this session, learn how the C3 Platform on AWS is architected to accelerate the development of modern AI applications. Hear how customers and partners have used the C3 Type System’s data-object centric abstraction layer to realize 10–100x productivity gains when building complex AI/ML applications. In addition, hear how global organizations are using C3 on AWS to solve complex business challenges, from optimizing the supply network, to predicting asset failure, to identifying fraud and money laundering. This presentation is brought to you by AWS partner, C3. Session Jim Hassman
AIM301-R - [REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour. Session Hagay Lupesko
Seungjai Min
AIM301-R1 - [REPEAT 1] Deep Learning for Developers: An Introduction, Featuring Samsung SDS Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Session Nathaniel Slater
Seungjai Min
AIM301-ROFA - [OVERFLOW] [REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS (Blue-Aria) Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Overflow
AIM301-ROFB - [OVERFLOW] [REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS (Blue-Bellagio) Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Overflow
AIM301-ROFM - [OVERFLOW] [REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS (Blue-Mirage) Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Overflow
AIM301-ROFMGM - [OVERFLOW] [REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS (Blue-MGM) Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Overflow
AIM301-ROFV - [OVERFLOW] [REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS (Blue-Venetian) Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Overflow
AIM302 - Machine Learning at the Edge Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Session Julien Simon
Nimish Amlathe
Brian Kursar
AIM302-OFA - [OVERFLOW] Machine Learning at the Edge (Teal-Aria) Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Overflow
AIM302-OFB - [OVERFLOW] Machine Learning at the Edge (Teal-Bellagio) Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Overflow
AIM302-OFM - [OVERFLOW] Machine Learning at the Edge (Teal-Mirage) Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Overflow
AIM302-OFMGM - [OVERFLOW] Machine Learning at the Edge (Teal-MGM) Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Overflow
AIM302-OFN - [OVERFLOW] Machine Learning at the Edge (3-Nuvola) Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Overflow
AIM302-OFS - [OVERFLOW] Machine Learning at the Edge (3-Scamall) Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Overflow
AIM302-OFV - [OVERFLOW] Machine Learning at the Edge (Teal-Venetian) Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity. Overflow
AIM303-R - [REPEAT] Create Smart and Interactive Apps with Intelligent Language Services on AWS Amazon brings natural language processing, automatic speech recognition, text-to-speech services, and neural machine translation technologies within the reach of every developers. In this session, learn how to add intelligence to any application with machine learning services that provide language and chatbot functions. See how others are defining and building the next generation of apps that can hear, speak, understand, and interact with the world around us. Session Niranjan Hira
Vikram Anbazhagan
Dave Mace
AIM303-R1 - [REPEAT 1] Create Smart and Interactive Apps with Intelligent Language Services on AWS Amazon brings natural language processing, automatic speech recognition, text-to-speech services, and neural machine translation technologies within the reach of every developers. In this session, learn how to add intelligence to any application with machine learning services that provide language and chatbot functions. See how others are defining and building the next generation of apps that can hear, speak, understand, and interact with the world around us. Session Niranjan Hira
Vikram Anbazhagan
Narsimha Rao Polisetty
Raj Raina
AIM303-R2 - [REPEAT 2] Create Smart and Interactive Apps with Intelligent Language Services on AWS Amazon brings natural language processing, automatic speech recognition, text-to-speech services, and neural machine translation technologies within the reach of every developers. In this session, learn how to add intelligence to any application with machine learning services that provide language and chatbot functions. See how others are defining and building the next generation of apps that can hear, speak, understand, and interact with the world around us. Session David Pearson
Niranjan Hira
Vikram Anbazhagan
Davis Addy
AIM304 - Transform the Modern Contact Center Using Machine Learning and Analytics Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception. Session Niranjan Hira
Paul Zhao
Yasser El-Haggan
Byron Guernsey
AIM304-OFA - [OVERFLOW] Transform the Modern Contact Center Using Machine Learning and Analytics (Teal-Aria) Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception. Overflow
AIM304-OFB - [OVERFLOW] Transform the Modern Contact Center Using Machine Learning and Analytics (Teal-Bellagio) Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception. Overflow
AIM304-OFM - [OVERFLOW] Transform the Modern Contact Center Using Machine Learning and Analytics (Teal-Mirage) Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception. Overflow
AIM304-OFMGM - [OVERFLOW] Transform the Modern Contact Center Using Machine Learning and Analytics (Teal-MGM) Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception. Overflow
AIM304-OFV - [OVERFLOW] Transform the Modern Contact Center Using Machine Learning and Analytics (Teal-Venetian) Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception. Overflow
AIM305-R - [REPEAT] Automatically Extract Metadata Using Computer Vision & Language AI Services Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution. Builders Session Sireesha Muppala
AIM305-R1 - [REPEAT 1] Automatically Extract Metadata Using Computer Vision & Language AI Services Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution. Builders Session Sireesha Muppala
AIM305-R2 - [REPEAT 2] Automatically Extract Metadata Using Computer Vision & Language AI Services Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution. Builders Session Gautam Srinivasan
AIM305-R3 - [REPEAT 3] Automatically Extract Metadata Using Computer Vision & Language AI Services Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution. Builders Session Sireesha Muppala
AIM305-R4 - [REPEAT 4] Automatically Extract Metadata Using Computer Vision & Language AI Services Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution. Builders Session Gautam Srinivasan
AIM305-R5 - [REPEAT 5] Automatically Extract Metadata Using Computer Vision & Language AI Services Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution. Builders Session Sireesha Muppala
AIM305-R6 - [REPEAT 6] Automatically Extract Metadata Using Computer Vision & Language AI Services Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution. Builders Session Sireesha Muppala
AIM306-R - [REPEAT] Build Custom Models for AWS DeepLens with Amazon SageMaker In this session, you will learn how to build and deploy computer vision models using the AWS DeepLens deep-learning-enabled video camera and Amazon SageMaker. Builders Session Mahendra Bairagi
AIM306-R1 - [REPEAT 1] Build Custom Models for AWS DeepLens with Amazon SageMaker In this session, you will learn how to build and deploy computer vision models using the AWS DeepLens deep-learning-enabled video camera and Amazon SageMaker. Builders Session Mahendra Bairagi
AIM306-R2 - [REPEAT 2] Build Custom Models for AWS DeepLens with Amazon SageMaker In this session, you will learn how to build and deploy computer vision models using the AWS DeepLens deep-learning-enabled video camera and Amazon SageMaker. Builders Session Mahendra Bairagi
AIM307-R - [REPEAT] Deep Dive on Amazon Rekognition, ft. Tinder & News UK Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications. Session Jon Turow
Tom Jacques
Rudi De Sousa
AIM307-R1 - [REPEAT 1] Deep Dive on Amazon Rekognition, ft. Pinterest Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications. Session Ranju Das
Jon Turow
Vanja Josifovski
AIM307-ROFA - [OVERFLOW] [REPEAT] Deep Dive on Amazon Rekognition (Yellow-Aria) Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications. Overflow
AIM307-ROFB - [OVERFLOW] [REPEAT] Deep Dive on Amazon Rekognition (Yellow-Bellagio) Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications. Overflow
AIM307-ROFM - [OVERFLOW] [REPEAT] Deep Dive on Amazon Rekognition (Yellow-Mirage) Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications. Overflow
AIM307-ROFMGM - [OVERFLOW] [REPEAT] Deep Dive on Amazon Rekognition (Yellow-MGM) Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications. Overflow
AIM307-ROFV - [OVERFLOW] [REPEAT] Deep Dive on Amazon Rekognition (Yellow-Venetian) Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications. Overflow
AIM308-R - [REPEAT] AWS DeepLens Projects: Tips & Tricks In this session, get general help on how to build or extend a project with AWS DeepLens. Builders Session Mahendra Bairagi
AIM308-R1 - [REPEAT 1] AWS DeepLens Projects: Tips & Tricks In this session, get general help on how to build or extend a project with AWS DeepLens. Builders Session Bradley Kenstler
AIM308-R2 - [REPEAT 2] AWS DeepLens Projects: Tips & Tricks In this session, get general help on how to build or extend a project with AWS DeepLens. Builders Session Bradley Kenstler
AIM309-R - [REPEAT] Machine Learning and Predicting Risk In this session, attendees work with data from the National Highway Traffic Safety Administration containing accident, vehicle, and person information for certain years in the US. They predict the fatality in a specific car accident with machine learning, using the ML Data Readiness Package based on KNIME from AWS Marketplace. Builders Session Vikrant Kahlir
AIM309-R1 - [REPEAT 1] Machine Learning and Predicting Risk In this session, attendees work with data from the National Highway Traffic Safety Administration containing accident, vehicle, and person information for certain years in the US. They predict the fatality in a specific car accident with machine learning, using the ML Data Readiness Package based on KNIME from AWS Marketplace. Builders Session Edouard Kachelmann
AIM309-R2 - [REPEAT 2] Machine Learning and Predicting Risk In this session, attendees work with data from the National Highway Traffic Safety Administration containing accident, vehicle, and person information for certain years in the US. They predict the fatality in a specific car accident with machine learning, using the ML Data Readiness Package based on KNIME from AWS Marketplace. Builders Session Vikrant Kahlir
AIM309-R3 - [REPEAT 3] Machine Learning and Predicting Risk In this session, attendees work with data from the National Highway Traffic Safety Administration containing accident, vehicle, and person information for certain years in the US. They predict the fatality in a specific car accident with machine learning, using the ML Data Readiness Package based on KNIME from AWS Marketplace Builders Session Vikrant Kahlir
AIM309-R4 - [REPEAT 4] Machine Learning and Predicting Risk In this session, attendees work with data from the National Highway Traffic Safety Administration containing accident, vehicle, and person information for certain years in the US. They predict the fatality in a specific car accident with machine learning, using the ML Data Readiness Package based on KNIME from AWS Marketplace. Builders Session Madhu Raman
AIM310 - How Emory University Built an NLP Platform Using Apache MXNet and GluonNLP Building natural language processing (NLP) models just got easier and faster. Join us for a discussion with Emory University on implementing state-of-the-art NLP models using Apache MXNet and GluonNLP. Emory University shares best practices and key takeaways from developing their Evolution of Language and Information Technology (ELIT) platform on top of GluonNLP. Chalk Talk Sukwon Kim
Sheng Zha
Jinho Choi
Gary Lai
AIM311 - Machine Learning and Predictive Quality Management From refined products to heavy crude, Four-Path Ultrasonic Flow Meters offers the capability to minimize measurement uncertainty of liquid hydrocarbons. Attendees work to build a machine learning (ML) predictive quality management (PQM) solution on AWS to proactively predict the health of the ultrasonic flow meters. This is done using the ML Data Readiness Package based on KNIME, from AWS Marketplace. Another PQM example for attendees to explore uses features extracted from motor current measured with a current probe and an oscilloscope on two phases measured under different speeds, load moments, and load forces. ML is used to proactively classify whether the motor has intact or defective components. A third PQM example involves using raw process sensor data from a hydraulic test rig with a primary working and a secondary cooling-filtration circuit, connected via the oil tank. They then use ML on AWS to proactively predict the cooler condition, hydraulic accumulator condition, internal pump leakage condition, and valve condition. Builders Session Madhu Raman
AIM313 - Build a Babel Fish with Machine Learning Language Services In the novel, “The Hitchhiker's Guide to the Galaxy,” Douglas Adams described a Babel fish as a “small, yellow, and leech-like” device that you stick in your ear. In Star Trek, it is known simply as the universal language translator. Whatever you call it, there is no doubting the practical value of a device that is capable of translating any language into another. In this workshop, learn how to build a babel fish app that recognizes voice and converts it to text (speech-to-text), translates the text to a language of your choice, and converts translated text to synthesized speech (text-to-speech). Workshop Tomasz Stachlewski
Maciej Chmielarz
AIM314 - Create a "Question and Answer" Bot with Amazon Lex and Amazon Alexa A recent poll showed that 44% of customers would rather talk to a chatbot than a human for customer support. In this workshop, we show you how to deploy a "question and answer" bot using two open-source projects: QnABot and Lex-Web-UI. You get started quickly using Amazon Lex, Alexa, and Amazon Elasticsearch Service to provide a conversational chatbot interface. You enhance this solution using AWS Lambda and integrate with Amazon Connect. Workshop Bob Potterveld
Bob Strahan
AIM315-R - [REPEAT] Deep Learning for Edge to Cloud In this workshop, you step into the role of a startup that has assumed the challenge of providing a new type of EDM music festival experience. Your goal is to use machine learning (ML) and IoT to develop a connected fan experience that enhances the festival. Come and get hands-on experience with Amazon SageMaker with Intel C5 instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda as you build and deploy an ML model and then run inference on it from the cloud and from edge devices. Workshop Bradley Kenstler
Mahendra Bairagi
AIM315-R1 - [REPEAT 1] Deep Learning for Edge to Cloud In this workshop, you step into the role of a startup that has assumed the challenge of providing a new type of EDM music festival experience. Your goal is to use machine learning (ML) to develop a connected fan experience that enhances the festival. Come and get hands-on experience with Amazon SageMaker, AWS DeepLens, Amazon Rekognition, and AWS Lambda as you build and deploy an ML model and then run inference on it from edge devices. Workshop Bradley Kenstler
Mahendra Bairagi
AIM316-R - [REPEAT] Get Started with Deep Learning and Computer Vision Using AWS DeepLens If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Also learn to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application. Workshop Jyothi Nookula
Kashif Imran
AIM316-R1 - [REPEAT 1] Get Started with Deep Learning and Computer Vision Using AWS DeepLens If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Also learn to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application. Workshop Jyothi Nookula
Kashif Imran
AIM316-R2 - [REPEAT 2] Get Started with Deep Learning and Computer Vision Using AWS DeepLens If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Also learn to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application. Workshop Jyothi Nookula
Kashif Imran
AIM317-R - [REPEAT] Scalable Text Mining for ETL Solutions While many ETL tools can handle structured data, very few can reliably process unstructured data and documents. In this session, learn how you can use Amazon Comprehend to address unstructured data extraction and transformation at scale so that key information can be extracted and used downstream for data integration and analytics. Builders Session DIMITRIOS SOULIOS
AIM317-R1 - [REPEAT 1] Scalable Text Mining for ETL Solutions While many ETL tools can handle structured data, very few can reliably process unstructured data and documents. In this session, learn how you can use Amazon Comprehend to address unstructured data extraction and transformation at scale so that key information can be extracted and used downstream for data integration and analytics. Builders Session DIMITRIOS SOULIOS
AIM317-R2 - [REPEAT 2] Scalable Text Mining for ETL Solutions While many ETL tools can handle structured data, very few can reliably process unstructured data and documents. In this session, learn how you can use Amazon Comprehend to address unstructured data extraction and transformation at scale so that key information can be extracted and used downstream for data integration and analytics. Builders Session Nino Bice
AIM317-R3 - [REPEAT 3] Scalable Text Mining for ETL Solutions While many ETL tools can handle structured data, very few can reliably process unstructured data and documents. In this session, learn how you can use Amazon Comprehend to address unstructured data extraction and transformation at scale so that key information can be extracted and used downstream for data integration and analytics. Builders Session Nino Bice
AIM318-R - [REPEAT] Create the Voices You Want with Amazon Polly Many of today's text-to-speech systems limit your choices to a few voices. If these voices aren't right for your needs, the process of adding more voices is usually costly and time consuming. Learn how you can use Amazon Polly for speech production and to modify voices for a range of uses, from game development and telephony to web publishing. Builders Session Remus Mois
AIM318-R1 - [REPEAT 1] Create the Voices You Want with Amazon Polly Many of today's text-to-speech systems limit your choices to a few voices. If these voices aren't right for your needs, the process of adding more voices is usually costly and time consuming. Learn how you can use Amazon Polly for speech production and to modify voices for a range of uses, from game development and telephony to web publishing. Builders Session Remus Mois
AIM319-R - [REPEAT] Build Multichannel Conversational Interfaces Using Amazon Lex Learn how to build a multichannel conversational interface that uses a preprocessing layer in front of Amazon Lex, or route messages to other specialized bots. Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, QuickBooks, Microsoft Dynamics, Zendesk, and HubSpot. Also learn how to integrate with any other application by combining the Amazon Lex API and Amazon API Gateway to integrate your Amazon Lex bot with any messaging service. Builders Session Mona Diab
AIM319-R1 - [REPEAT 1] Build Multichannel Conversational Interfaces Using Amazon Lex Learn how to build a multichannel conversational interface that uses a preprocessing layer in front of Amazon Lex, or route messages to other specialized bots. Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, QuickBooks, Microsoft Dynamics, Zendesk, and HubSpot. Also learn how to integrate with any other application by combining the Amazon Lex API and Amazon API Gateway to integrate your Amazon Lex bot with any messaging service. Builders Session Mona Diab
AIM319-R2 - [REPEAT 2] Build Multichannel Conversational Interfaces Using Amazon Lex Learn how to build a multichannel conversational interface that uses a preprocessing layer in front of Amazon Lex, or route messages to other specialized bots. Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, QuickBooks, Microsoft Dynamics, Zendesk, and HubSpot. Also learn how to integrate with any other application by combining the Amazon Lex API and Amazon API Gateway to integrate your Amazon Lex bot with any messaging service. Builders Session Mona Diab
AIM319-R3 - [REPEAT 3] Build Multichannel Conversational Interfaces Using Amazon Lex Learn how to build a multichannel conversational interface that uses a preprocessing layer in front of Amazon Lex, or route messages to other specialized bots. Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, QuickBooks, Microsoft Dynamics, Zendesk, and HubSpot. Also learn how to integrate with any other application by combining the Amazon Lex API and Amazon API Gateway to integrate your Amazon Lex bot with any messaging service. Builders Session Harshal Pimpalkhute
AIM319-R4 - [REPEAT 4] Build Multichannel Conversational Interfaces Using Amazon Lex Learn how to build a multichannel conversational interface that uses a preprocessing layer in front of Amazon Lex, or route messages to other specialized bots. Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, QuickBooks, Microsoft Dynamics, Zendesk, and HubSpot. Also learn how to integrate with any other application by combining the Amazon Lex API and Amazon API Gateway to integrate your Amazon Lex bot with any messaging service. Builders Session Harshal Pimpalkhute
AIM320-R - [REPEAT] Machine Learning Transcription: Tips & Tricks Amazon Transcribe is an automatic speech recognition service that makes it easy for developers to add speech-to-text capability to their applications. Join us and learn how you can build machine transcription into your existing workflows, such as transcribing videos for search engines or captioning.   Builders Session Paul Zhao
AIM320-R1 - [REPEAT 1] Machine Learning Transcription: Tips & Tricks Amazon Transcribe is an automatic speech recognition service that makes it easy for developers to add speech-to-text capability to their applications. Join us and learn how you can build machine transcription into your existing workflows, such as transcribing videos for search engines or captioning. Builders Session Paul Zhao
AIM321 - Improve Your Customer Experience with Machine Translation Machine Translation powers Amazon’s international expansion. Sign up to learn how you can leverage Amazon Translate to increase customer satisfaction, cut down response times, and build a more efficient customer support operation. For example, you can add real-time translation to chat, email, and helpdesk so an English-speaking agent can communicate with customers in their preferred language, or translate your knowledge base into multiple languages to make it accessible to customers and employees around the world.   Chalk Talk Tom Butler
Yoni Friedman
Niranjan Hira
AIM322-R - [REPEAT] Architecture Patterns for Natural Language Solutions: Tips & Tricks Amazon brings natural language processing, automatic speech recognition, text-to-speech, and neural machine translation technologies within reach of every developer. Come to this session and learn how to build smarter applications or automate workflows with AWS language services. Builders Session Niranjan Hira
AIM322-R1 - [REPEAT 1] Architecture Patterns for Natural Language Solutions: Tips & Tricks Amazon brings natural language processing, automatic speech recognition, text-to-speech, and neural machine translation technologies within reach of every developer. Come to this session and learn how to build smarter applications or automate workflows with AWS language services. Builders Session Niranjan Hira
AIM323-R - [REPEAT] Build a Searchable Image Library with Amazon Rekognition Join us for a deep dive on building a searchable image library using Amazon Rekognition. We walk though creating a search index for objects and scenes so you can quickly retrieve images using labels created from automatic metadata extraction. Also learn how to use AWS Lambda to automatically maintain your image library. Builders Session Dathu Patil
AIM323-R1 - [REPEAT 1] Build a Searchable Image Library with Amazon Rekognition Join us for a deep dive on building a searchable image library using Amazon Rekognition. We walk though creating a search index for objects and scenes so you can quickly retrieve images using labels created from automatic metadata extraction. Also learn how to use AWS Lambda to automatically maintain your image library. Builders Session Dathu Patil
AIM323-R2 - [REPEAT 2] Build a Searchable Image Library with Amazon Rekognition Join us for a deep dive on building a searchable image library using Amazon Rekognition. We walk though creating a search index for objects and scenes so you can quickly retrieve images using labels created from automatic metadata extraction. Also learn how to use AWS Lambda to automatically maintain your image library. Builders Session Dathu Patil
AIM324-R - [REPEAT] Analyze Live Video Streams with Amazon Rekognition Video In this session, learn how to use Amazon Rekognition Video with Amazon Kinesis to receive and process video streams. We walk through recognizing faces, giving access to Amazon Kinesis Data Streams, as well as starting and reading the streaming video analysis. Whether you are building a "Who's Who" app similar to the royal wedding, or you need to identify athletes in real time, it is simple to add intelligent video analysis to your live streams using Amazon Rekognition. Builders Session Wen-ming Ye
AIM324-R1 - [REPEAT 1] Analyze Live Video Streams with Amazon Rekognition Video In this session, learn how to use Amazon Rekognition Video with Amazon Kinesis to receive and process video streams. We walk through recognizing faces, giving access to Amazon Kinesis Data Streams, as well as starting and reading the streaming video analysis. Whether you are building a "Who's Who" app similar to the royal wedding, or you need to identify athletes in real time, it is simple to add intelligent video analysis to your live streams using Amazon Rekognition. Builders Session Wen-ming Ye
AIM325-R - [REPEAT] Amazon SageMaker: Prebuilt Algorithms In this session, learn how to use the range of built-in, high-performance machine learning algorithms that come with Amazon SageMaker. Builders Session Chaitanya Hazarey
AIM325-R1 - [REPEAT 1] Amazon SageMaker: Prebuilt Algorithms In this session, learn how to use the range of built-in, high-performance machine learning algorithms that come with Amazon SageMaker. Builders Session Vineet Khare
AIM325-R2 - [REPEAT 2] Amazon SageMaker: Prebuilt Algorithms In this session, learn how to use the range of built-in, high-performance machine learning algorithms that come with Amazon SageMaker. Builders Session Chaitanya Hazarey
AIM325-R3 - [REPEAT 3] Amazon SageMaker: Prebuilt Algorithms In this session, learn how to use the range of built-in, high-performance machine learning algorithms that come with Amazon SageMaker. Builders Session Chaitanya Hazarey
AIM326-R - [REPEAT] Amazon SageMaker and TensorFlow: Tips & Tricks In this session, learn how to use TensorFlow in the Amazon SageMaker machine learning platform. Builders Session Kyle Johnson
AIM326-R1 - [REPEAT 1] Amazon SageMaker and TensorFlow: Tips & Tricks In this session, learn how to use TensorFlow in the Amazon SageMaker machine learning platform. Builders Session Kyle Johnson
AIM326-R2 - [REPEAT 2] Amazon SageMaker and TensorFlow: Tips & Tricks In this session, learn how to use TensorFlow in the Amazon SageMaker machine learning platform. Builders Session Kyle Johnson
AIM327-R - [REPEAT] Amazon SageMaker and Apache MXNet: Tips & Tricks In this session, learn how to use Apache MXNet in the Amazon SageMaker machine learning platform. Builders Session Sheng Zha
AIM327-R1 - [REPEAT 1] Amazon SageMaker and Apache MXNet: Tips & Tricks In this session, learn how to use Apache MXNet in the Amazon SageMaker machine learning platform. Builders Session Sheng Zha
AIM327-R2 - [REPEAT 2] Amazon SageMaker and Apache MXNet: Tips & Tricks In this session, learn how to use Apache MXNet in the Amazon SageMaker machine learning platform. Builders Session Sheng Zha
AIM328-R - [REPEAT] Amazon SageMaker and PyTorch: Tips & Tricks In this session, learn how to use PyTorch in the Amazon SageMaker machine learning platform. Builders Session Charles Frenzel
AIM328-R1 - [REPEAT 1] Amazon SageMaker and PyTorch: Tips & Tricks In this session, learn how to use PyTorch in the Amazon SageMaker machine learning platform. Builders Session Charles Frenzel
AIM328-R2 - [REPEAT 2] Amazon SageMaker and PyTorch: Tips & Tricks In this session, learn how to use PyTorch in the Amazon SageMaker machine learning platform. Builders Session Charles Frenzel
AIM329-R - [REPEAT] Amazon SageMaker and Chainer: Tips & Tricks In this session, learn how to use Chainer, an open-source deep learning framework written in Python, in the Amazon SageMaker machine learning platform. Builders Session Dheepan Ramanan
AIM329-R1 - [REPEAT 1] Amazon SageMaker and Chainer: Tips & Tricks In this session, learn how to use Chainer, an open-source deep learning framework written in Python, in the Amazon SageMaker machine learning platform. Builders Session Dheepan Ramanan
AIM330-R - [REPEAT] Amazon SageMaker: Custom Algorithms In this session, learn how to integrate custom algorithms into the Amazon SageMaker platform. Builders Session Sumit Thakur
AIM330-R1 - [REPEAT 1] Amazon SageMaker: Custom Algorithms In this session, learn how to integrate custom algorithms into the Amazon SageMaker platform. Builders Session Abhishek Mishra
AIM331-R - [REPEAT] Amazon SageMaker: Bring Your Own Framework In this session, learn how to use your own framework in a Docker container within the Amazon SageMaker platform. Builders Session Sergey Ermolin
AIM332 - Improve Accessibility Using Machine Learning Machine learning (ML) can help people with disabilities by using facial and object recognition, text-to-speech, automatic translation, and transcription to create assistive applications. In this chalk talk, learn how to assemble ML APIs from AWS to help people in new ways. Chalk Talk Troy Larson
Robin Dautricourt
AIM333 - Unsupervised Learning with Amazon SageMaker How do you use machine learning with data that isn't labeled? The unsupervised learning capabilities of Amazon SageMaker can easily handle unlabeled data. In this chalk talk, we discuss the intricacies of unsupervised algorithms that are built into Amazon SageMaker, including clustering with k-means and anomaly detection with Random Cut Forest. Chalk Talk Tom Faulhaber
AIM334 - Build Models for Aerial Images Using Amazon SageMaker There are unique challenges to building highly accurate models that detect small objects in aerial and overhead imagery. In this chalk talk, we dive deep into using convolutional neural networks (CNNs) with Amazon SageMaker in order to build and train aerial object detection models. We build advanced models using AWS public datasets, such as SpaceNet and LandSat, as we work with DigitalGlobe's GBDX Notebooks. Chalk Talk Alistair McLean
Kate Werling
AIM335 - Run XGBoost with Amazon SageMaker XGBoost makes applying machine learning (ML) to real-world scenarios easy and powerful. Amazon SageMaker has XGBoost built in, and this enables the transition of ML models from training to production at scale. In this chalk talk, we discuss the details of using XGBoost on Amazon SageMaker, and we cover how to train and deploy ML models in a way that is simple, powerful, and scalable. Chalk Talk Yash Pant
AIM336-R - [REPEAT] Create Live Sports Analytics with Machine Learning Machine learning provides a new way of looking at sports analytics and experiencing sporting events. Custom-built models can now be used to provide live sports analytics. In this chalk talk, learn how to train models that can detect sports events, such as a substitutions, goals, and red cards, using services like Amazon Rekognition and Amazon Transcribe. Chalk Talk Joe Tighe
AIM336-R1 - [REPEAT 1] Create Live Sports Analytics with Machine Learning Machine learning provides a new way of looking at sports analytics and experiencing sporting events. Custom-built models can now be used to provide live sports analytics. In this chalk talk, learn how to train models that can detect sports events, such as a substitutions, goals, and red cards, using services like Amazon Rekognition and Amazon Transcribe. Chalk Talk David Pearson
Joe Tighe
AIM337 - Powering Multilingual Video Transcription, Translation, and Search Automatic video transcription and translation can help make videos more available and accessible to a global audience in many languages, enabling your employees or customers to access, understand, and benefit from your content. In this chalk talk, we discuss how to transcribe videos, translate them in the required languages in a multilingual application, and enable video search in the viewer’s preferred language—all in an automated and cost-effective manner. Chalk Talk Rob Dachowski
AIM338 - Crafting a Conversational Platform Strategy Your leadership has asked you for a business case for chatbots. You know it’s a good idea, but you don’t have all the answers. What should you be looking for in a conversational platform as a consumer-facing organization? What do you need to consider for that business case? What are the opportunities you shouldn’t be missing? This chalk talk is an interactive session where you get the opportunity to work through real-life scenarios, share your experiences, and learn from ours. Chalk Talk Harshal Pimpalkhute
Gillian Armstrong
AIM339 - Building Online Communities Without Language Barriers Most communities are currently language specific. To engage in a community the user must speak the language native language. In this chalk talk, we will discuss how you can integrate state of the art machine translation to enable users to effectively share and consume content in their language of choice and create truly global communities. Broadening the user base through seamless language translation helps developers increase participation and contribution, ultimately improving the economics of their applications. Chalk Talk Yoni Friedman
Kashif Imran
AIM340 - Build an Intelligent Multi-Modal User Agent with Voice and NLU Sophisticated AI capabilities can help us manage the exploding number of information sources and tools required to perform our daily tasks. In this chalk talk, we describe how intelligent agents can be designed to quickly and efficiently complete tasks delegated by users. To build this intelligent agent, we combine a number of AWS services, such as Amazon Polly, Amazon Lex, Amazon Rekognition, Amazon Sumerian, and Amazon ElastiCache along with other technologies, such as CLIPS and Reinforcement Learning. Come hear us discuss the project’s architecture, implementation, and demo progress made to date.   Chalk Talk Keith Steward
AIM341 - Build a Visual Search Engine Using Amazon SageMaker and AWS Fargate Visual search engines have a growing importance at companies like Pinterest as well as at e-commerce companies like Amazon.com and Gilt. In this chalk talk, we show you how to build a visual search engine using Amazon SageMaker and AWS Fargate. Chalk Talk Thomas Delteil
Girish Dilip Patil
AIM342-R - [REPEAT] Create a Serverless Searchable Media Library Companies have ever-growing media libraries, making them increasingly difficult to index and search. In this session, we describe how to maintain your library by using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend to perform automatic metadata extraction from image, video, and audio files. We show you how to then use this metadata to build a serverless media library that can be filtered by image tags, celebrities, and more. Chalk Talk Liam Morrison
AIM342-R1 - [REPEAT 1] Create a Serverless Searchable Media Library Companies have ever-growing media libraries, making them increasingly difficult to index and search. In this session, we describe how to maintain your library by using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend to perform automatic metadata extraction from image, video, and audio files. We show you how to then use this metadata to build a serverless media library that can be filtered by image tags, celebrities, and more. Chalk Talk Liam Morrison
AIM343 - Build Automated Video Social Posts for Player Records and Highlights In the world of sports entertainment, the fast pace of live events makes it difficult to keep up with new records and highlights that occur during games. In this session, learn how machine learning can combine internal statistics feeds with image player recognition to log when new player records are set. See how this solution uses Amazon Rekognition to identify the player, AWS Lambda to determine if the play is a new record for the particular athlete, and then automatically creates an image to share on social media that highlights the player in action. Chalk Talk Lidio Ramalho
Samir Araujo
AIM344 - [NEW LAUNCH!] Introducing Amazon Forecast  Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. Come learn more. Session bindu reddy
Jan Gasthaus
Lluis Canet
AIM345 - [NEW LAUNCH!] Introducing Amazon Forecast - workshop Earlier today, we announced a new service, Amazon Forecast. Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series (such as sales and web traffics) data with additional values (such as product descriptions and holidays) to build accurate forecasts. The service uses AutoML technology to automatically create forecasting models after learning from you data, reducing forecasting time from months to hours. Join this workshop to learn more. Workshop bindu reddy
Cheng Yang
Jan Gasthaus
AIM346 - NLP in Healthcare to Predict Adverse Events with Amazon SageMaker In healthcare, pharmacovigilance is key to improving patient outcomes. The prediction of adverse events will enable pharmaceutical companies and drug distributors in accurately meeting their pharmacovigilance requirements and scaling their operations. In this chalk talk, we discuss how Amazon SageMaker can be used to classify large-scale agent and reporter interaction summaries. We also discuss natural language processing (NLP) methods and results. Chalk Talk Mayank Thakkar
Kessler Garin
AIM347 - Detecting Financial Market Manipulation Using Machine Learning Researchers from the University of Michigan and Georgia Tech, in collaboration with the AWS Research Initiative, have developed new techniques to identify financial market manipulation in high-volume, high-velocity market data streams. They are using a combination of data-driven and model-based techniques to identify financial market manipulation. In this session, we discuss the use of machine learning using Amazon SageMaker to study, process, and analyze huge volumes of data to prevent financial market manipulation. Chalk Talk Sanjay Padhi
Michael Wellman
AIM348 - Translating Web Content Easily with Language Services from AWS Providing multilingual content represents a great opportunity for site owners. Although English is the dominant language of the web, native English speakers comprise only 26% of the total online audience. In this chalk talk, we discuss how you can make your web content more accessible with text-to-speech and machine translation. By offering written and audio versions of your content in multiple languages, you can meet the needs of a larger international audience. Chalk Talk Tomasz Stachlewski
Steven Word
AIM349-R - [REPEAT] Create a Custom Celebrity List for Your Media Assets When indexing large amounts of media files, it can become difficult to search through them to find certain objects and individuals. In this session, we show you how creating a custom celebrity list enables you to index your media files by the people you train it to recognize. Come and see how this solution can serve as the foundation for creating automated sports highlight reels, building face-based user verification systems, and more. Chalk Talk Suman Koduri
AIM349-R1 - [REPEAT 1] Create a Custom Celebrity List for Your Media Assets When indexing large amounts of media files, it can become difficult to search through them to find certain objects and individuals. In this session, we show you how creating a custom celebrity list enables you to index your media files by the people you train it to recognize. Come and see how this solution can serve as the foundation for creating automated sports highlight reels, building face-based user verification systems, and more. Chalk Talk Suman Koduri
AIM350 - Bring Your Own Apache MXNet and TensorFlow Scripts to Amazon SageMaker Amazon SageMaker enables you to bring your existing Apache MXNet or TensorFlow script for your machine learning models. In this session, we walk through the details of bringing your own script for training your models at scale. We also go into detail on using local containers for repeated experiments for ease of use and scalability. Chalk Talk Marcelo Bossi
Muhyun Kim
AIM351 - Harness the Power of Crowdsourcing with Amazon Mechanical Turk Amazon Mechanical Turk operates a marketplace for crowdsourcing, and developers can build human intelligence directly into their applications through a simple API. With access to a diverse, on-demand workforce, companies can leverage the power of the crowd for a range of tasks, from ML training and automating manual tasks to generating human insights. In this session, we cover key concepts for Mechanical Turk, and we share best practices for how to integrate and scale your crowdsourced application. By the end of this session, expect to have a general understanding of Mechanical Turk and know how to get started harnessing the power of the crowd. Session Jessica Locke
Keisha Phillips
Jeffrey Fenchel
AIM352 - Business Process Automation Using Crowdsourcing While technology continues to improve, there are still many things that human beings can do much more effectively than computers, such as performing data deduplication or content moderation. Traditionally, such tasks have been accomplished by hiring a large temporary workforce—which is time consuming, expensive, and difficult to scale—or have gone undone. However, businesses or developers can use Amazon Mechanical Turk (Mechanical Turk) to access thousands of on-demand workers—and then integrate the results of that work directly into their business processes and systems. In this session, learn how enterprises are using Mechanical Turk to scale and automate their human-powered workflow. Session Mark Chien
Andy Hilal
DAVID FALCK
AIM353 - Gathering Data with Amazon Mechanical Turk Amazon Mechanical Turk (Mechanical Turk) aims to make accessing human intelligence simple, scalable, and cost-effective. The diversification and the scale of the Mechanical Turk workforce enables you to collect a breadth of information that would be almost impossible to do otherwise. Learn how Mechanical Turk can make it easy to gather the information you need online. Builders Session Janak Mayer
AIM354 - Build Human-in-the-Loop Systems with AWS Lambda and Mechanical Turk Building human-in-the-loop solutions can be very effective, but integrating humans into existing ML or business process workflows can be complex. Learn how you can easily connect the Amazon Mechanical Turk (Mechanical Turk) on-demand human intelligence platform with other AWS services, such as Amazon S3, Amazon Lex, Amazon Polly, and Amazon Rekognition with AWS Lambda. Builders Session Samuel Henry
AIM355 - Design Tasks for Quality Results from the Crowd Using Amazon Mechanical Turk In this session, learn how to achieve high-quality output from crowdsourcing through well-designed and clearly explained tasks, and verify your crowdsourced data. Builders Session Samuel Henry
AIM356 - Engage the Crowd for Better Results from Amazon Mechanical Turk Consistent feedback and communication, as well as the use of quality controls, can impact your ability to derive quality content from your workers at scale. Join us for best practices for engaging the crowd. Builders Session Sourabh Miglani
AIM357-R - [REPEAT] Developing Best Practices for Machine Translation Faced with higher volumes of content that require translation and with shorter completion times, more organizations are weighing the pros and cons of machine translation (MT) as a viable solution to tackle time-critical projects. Learn how you can adapt MT for your translation workflows using best practices, such as caching for cost savings, custom glossary for improved quality, and combining human and machine translation to achieve the best results. Builders Session Yoni Friedman
AIM357-R1 - [REPEAT 1] Developing Best Practices for Machine Translation Faced with higher volumes of content that require translation and with shorter completion times, more organizations are weighing the pros and cons of machine translation (MT) as a viable solution to tackle time-critical projects. Learn how you can adapt MT for your translation workflows using best practices, such as caching for cost savings, custom glossary for improved quality, and combining human and machine translation to achieve the best results. Builders Session Yoni Friedman
AIM358-R - [REPEAT] Human-in-the-Loop for Machine Learning Businesses can benefit from both the efficiency of machine learning (ML) as well as the quality of human judgement. An increasing part of the ML solution is human-in-the-loop (HITL), where human feedback is provided to evaluate the output of ML algorithms, i.e., to determine its validity and help refine the result. An example is image classification, where the task might be too ambiguous for a purely mechanical solution and too vast for even a large team of human experts. In this session, learn how to effectively incorporate human-in-the-loop in your ML projects to achieve higher accuracy and better results with Amazon Mechanical Turk (Mechanical Turk). Chalk Talk Trenton Lipscomb
AIM358-R1 - [REPEAT 1] Human-in-the-Loop for Machine Learning Businesses can benefit from both the efficiency of machine learning (ML) as well as the quality of human judgement. An increasing part of the ML solution is human-in-the-loop (HITL), where human feedback is provided to evaluate the output of ML algorithms, i.e., to determine its validity and help refine the result. An example is image classification, where the task might be too ambiguous for a purely mechanical solution and too vast for even a large team of human experts. In this session, learn how to effectively incorporate human-in-the-loop in your ML projects to achieve higher accuracy and better results with Amazon Mechanical Turk (Mechanical Turk). Chalk Talk Trenton Lipscomb
AIM359 - Using Amazon Mechanical Turk to Crowdsource Data Collection Companies and researchers are increasingly turning to low-cost crowdsourcing platforms, like Amazon Mechanical Turk (Mechanical Turk), for data collection. Whether you’re trying to assemble all the IMDB entries for a long list of movies or find the websites of several hundred companies, Mechanical Turk enables you to easily gather the information you need. In this session, we share how you can get started with crowdsourced data collection using Mechanical Turk, and how some companies are using it today. Chalk Talk Dave Schultz
AIM360 - Delight Your Customers through Natural Language Conversational Experiences In this chalk talk, we first describe various use cases to engage customers through natural language conversations. We then showcase how to prepare, implement, and continuously improve such solutions. We use Amazon Lex to drive the chatbot interactions and capture user events in Amazon Pinpoint analytics. The event data is then used to measure user engagement and sentiment within conversations. Chalk Talk Ilan Sehayek
AIM361 - Building State-of-the-Art Computer Vision Models Using MXNet and Gluon Implementing computer vision (CV) models just got simpler and faster. In this chalk talk, learn how to implement CV models using MXNet and the Gluon CV Toolkit, which provides implementations of state-of-the-art deep learning algorithms in computer vision to help engineers, researchers, and students quickly prototype products, validate new ideas, and learn computer vision. Chalk Talk Tong He
Marcelo Bossi
AIM362 - Crowdsourcing Data Collection with Amazon Mechanical Turk One of the most expensive and time-consuming aspects of building your machine learning (ML) model is probably generating a high-quality dataset. Many times, all you have is a big bucket of raw, unlabeled data. Furthermore, the process of manually annotating massive datasets might be the most painful phase of your ML workflow. Crowdsourcing can be a great way to minimize the costs and the time it takes to collect and annotate data. Amazon Mechanical Turk makes accessing human intelligence simple, scalable, and cost-effective. In this workshop, learn how to use crowdsourcing to find still images to best represent scenes from a hit TV series, The Marvelous Mrs. Maisel, and identify and label items in those images to train an ML model. Workshop Janak Mayer
Michael Shim
AIM363 - [NEW LAUNCH!] Introducing Amazon Textract: Now in Preview Amazon Textract enables you to easily extract text and data from virtually any document. Today, companies process millions of documents by manually entering the data or using customized optical character recognition solutions, which are prone to error and consume valuable resources. Join us to learn how Amazon Textract uses machine learning to simplify document processing by enabling fast and accurate text and data extraction so you can process millions of documents in hours. Session Ranju Das
Wendy Tse
Bradley Christus
John Newton
AIM364 - [NEW LAUNCH!] Extract Insights from Millions of Documents with Amazon Textract When using optical character recognition technology to extract text from documents, it quickly becomes difficult to organize and analyze the extracted data. Amazon Textract uses machine learning to not only accurately extract text and structured data, but also perform key-value pairing and table detection so that extracted data doesn’t need additional manual work to become useful. We will discuss how you can use Amazon Textract to extract insights from millions of document by building a document search index, gaining insights from extracted text using NLP, and more. Chalk Talk Bhavesh Doshi
AIM365 - [NEW LAUNCH!] Introducing Amazon Personalize: Real-time Personalization and Recommendations Amazon Personalize is a fully-managed service that helps companies deliver personalized experiences, such as recommendations, search results, email campaigns and notifications. It brings over 20 years of experience in personalization from Amazon.com and puts it in the hands of developers with little or no machine learning experience. Amazon Personalize uses AutoML to automate the entire process of managing and processing data, choosing the right algorithm based on the data, and using the data to train and deploy custom machine learning models — all with a few simple API calls. Join us and learn how you can use Concierge to build engaging experiences that respond to user preferences and behavior in real-time. Session bindu reddy
Balakrishnan Narayanaswamy
Mallika Krishnamurthy
Mike pyland
AIM366 - [NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Inference Cost up to 75% Deploying deep learning applications at scale can be cost prohibitive due to the need for hardware acceleration to meet latency and throughput requirements of inference. Amazon Elastic Inference helps you tackle this problem by reducing the cost of inference by up to 75% with GPU-powered acceleration that can be right-sized to your application’s inference needs. In this session, learn about how to deploy TensorFlow, Apache MXNet, and ONNX models with Amazon Elastic Inference on Amazon EC2 and Amazon SageMaker. Hear from Autodesk on the positive impact of AI on tools used to design and make a better world. Learn about how Autodesk and the Autodesk AI Lab are using Amazon Elastic Inference to make it cost efficient to run these tools at scale. Session Sudipta Sengupta
Dominic Divakaruni
Liviu-Mihai Calin
Peter Jones
AIM367 - [NEW LAUNCH!] Introducing AWS DeepRacer  Developers start your engines! This breakout session will provide an introduction to the newly launched AWS DeepRacer. Learn about the basics of reinforcement learning, what’s under the hood and your opportunities to experience AWS DeepRacer for yourself.   Session Mike Miller
AIM368 - [NEW LAUNCH!] Introducing Amazon SageMaker RL - Build and Train Reinforcement Learning models on Amazon SageMaker Reinforcement Learning is an exciting area within machine learning that enables development of many intelligent applications such as autonomous vehicles and robots. The applications with Reinforcement Learning can span across many areas including energy management, financial portfolio management, operations research, natural language processing, and many more. In this interactive workshop, you will learn the basics of Reinforcement Learning (RL) and how you can build and train RL models with the newly announced Amazon SageMaker RL. We will model a simulation environment to represent real-world problems. Further, we will train RL models in this environment and tune them to obtain the required results. By the end of this workshop, you will become familiar with Reinforcement Learning and be able to use SageMaker RL for your own business problems to build intelligent applications. Workshop Leo Dirac
Saurabh Gupta
Urvashi Chowdhary
Vineet Khare
AIM369 - [NEW LAUNCH!] Introducing Amazon SageMaker Ground Truth: Build High-Quality and Accurate ML Training Datasets Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity using a machine learning technique called active learning. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, Ground Truth improves the ability to automatically label data resulting in high-quality training datasets. Session Pietro Perona
Warren Barkley
Rahul Sharma
Chad Wahlquist
AIM370 - [NEW LAUNCH!] Amazon SageMaker Ground Truth – A Deep Dive with an Interactive Workshop to Build High-Quality Training Datasets Amazon SageMaker Ground Truth is a newly announced service to enable you to build high-quality accurate training datasets quickly and easily. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity. You will set up effective labeling jobs for text and images. Specifically, you will work on exercises covering image and text classification, object detection with bounding boxes, and semantic segmentation of images. Additionally, you will configure and set up quality instructions for the public Amazon Mechanical Turk workforce as well as your own private workforce. The workshop will wrap up with the automated labeling feature to help you reduce costs and the time required to obtain high-quality training datasets. Workshop Vikram Madan
Arvind Jayasundar
Fedor Zhdanov
AIM371 - [NEW LAUNCH!] Accelerating Machine Learning with Amazon SageMaker and AWS Marketplace Until now, many customers spent time creating or searching for the right algorithm and model when using Amazon SageMaker. In this session you’ll learn how to build machine learning applications even faster by finding curated algorithms and model packages in AWS Marketplace and deploying them directly on Amazon SageMaker. Session Srini Sankaran
Garth Fort
AIM385 - Build an Event Registration Kiosk Powered by Facial Recognition Registering for an event and waiting in line to verify your ticket in order to enter is a difficult process. Machine learning provides a solution to this challenge by using facial recognition to streamline the event registration process. This minimizes lines and enables attendees to quickly register and enter an event. In this session, we share best practices for building an event registration kiosk powered by facial recognition, integrating it with third-party registration services, and creating a web-based kiosk application. Chalk Talk Junghee Kang
Muhyun Kim
AIM386-S - Accelerate AI/ML Adoption with Intel Processors and C3IoT on AWS Today, organizations deploy more AI/ML workloads on AWS than on any other cloud platform. The cloud has removed many of the challenges associated with scalability, and it’s never been easier or more cost effective to build custom and intelligent data models. In this session, learn how the C3 Platform leverages the full power of Intel Xeon Scalable processors on AWS to rapidly train, deploy, and operationalize AI/ML and big data applications like C3 Inventory Optimization and C3 Predictive Maintenance. In addition, a customer shares how these solutions helped achieve demonstrable value. This session is brought to you by AWS partner, Intel. Session Binay Ackalloor
Nikhil Krishnan
AIM390 - Machine Learning Your Eight-Year-Old Would Be Proud Of Come see examples of how Bebo uses Amazon SageMaker to power massive Fortnite tournaments every week. Traditional sports require referees, scorekeepers, field staff, and broadcast crews for every match. But esports are digital by nature. In this session, learn how machine learning and computer vision are enabling esports to occur at a massive scale. Learn how Bebo developed a model that can detect every victory and elimination, and can even prevent cheating on their tournament platform.  Session Furqan Rydhan
AIM395 - [NEW LAUNCH!] Easily add real-time recommendations to your applications with Amazon Personalize Machine learning is being increasingly used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions. Amazon Personalize is a fully managed service allows developers with no prior machine learning experience to easily build sophisticated personalization capabilities into their applications. Join this workshop and learn how you can deliver tailored recommendations for your customers. Workshop Vaibhav Sethi
Venkatesh Sreenivas
Balakrishnan Narayanaswamy
AIM396-S - ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle In this session, we cover best practices for enterprises that want to use powerful open-source technologies to simplify and scale their machine learning (ML) efforts. Learn how to use Apache Spark, the data processing and analytics engine commonly used at enterprises today, for data preparation as it unifies data at massive scale across various sources. We train models using TensorFlow, and we use MLflow to track experiment runs between multiple users within a reproducible environment. We then manage the deployment of models to production. We show you how MLflow can be used with any existing ML library and incrementally incorporated into an existing ML development process. This session is brought to you by AWS partner, Databricks. Session Corey Zumar
Tomas Nykodym
AIM397 - [NEW LAUNCH!] Introducing Amazon SageMaker Neo: Train Once Run Anywhere in the Cloud or at the Edge Join us as we introduce Amazon SageMaker Neo, a new capability of Amazon SageMaker that enables machine learning (ML) models to train once and run anywhere in the cloud or at the edge. ML models are trained and tuned to be accurate and deliver the right predictions. A critical aspect of these models is about performance. Developers spend a lot of time and effort to produce a model that runs efficiently on the target hardware platform. SageMaker Neo removes the barriers holding back developers from running and deploying models in the most optimized manner. With SageMaker Neo, ML models are optimized to run up to two times faster and consume less than a hundredth of the resources compared to typical models. Neo automatically optimizes models built on TensorFlow, Apache MXNet, PyTorch, ONNX, and XGboost and can be deployed on multiple hardware platforms including ARM, Intel, and Nvidia. In this chalk talk, we will dive deep into Amazon SageMaker Neo, where we will discuss the innovation driving the optimization and making machine learning models easy to train and deploy across hardware platforms. Chalk Talk Andrea Olgiati
Sukwon Kim
AIM398 - [NEW LAUNCH!] Introducing Amazon Comprehend Medical Amazon Comprehend Medical is a service that makes it easier for developers to leverage state-of-the-art machine learning to extract medical entities—such as medical condition and medication, its dosage, strength, and frequency—from unstructured text, such as doctor’s notes, clinical trial reports, and patient health records, with high accuracy. Come learn more about how you can use Amazon Comprehend Medical to help healthcare providers and clinical researchers reduce the cost, time, and effort in processing large amounts of unstructured medical text. Session Taha Kass-Hout
Matthew Trunnell
Arun Ravi
Anish Kejariwal
AIM399 - Let’s Talk about Reinforcement Learning with Amazon SageMaker RL Reinforcement learning has emerged as an exciting new technique in the world of machine learning (ML), where your ML models can achieve specific outcomes without the need for pre-labeled training data. Join us in this chalk talk as we discuss the newly announced Amazon SageMaker RL, which takes a different approach to training ML models. We dive deep into scenarios where there isn’t a right answer; instead, there is an optimal outcome for a given problem. At the end of this chalk talk, you will be familiar with Amazon SageMaker RL and understand how to use reinforcement learning for your businesses and build intelligent applications. Chalk Talk Sina Afrooze
AIM401-R - [REPEAT] Deep Learning Applications Using TensorFlow The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Session Julien Simon
Jill Fagan
AIM401-R1 - [REPEAT 1] Deep Learning Applications Using TensorFlow, ft. Siemens Financial Services The TensorFlow deep learning framework is used for developing diverse AI applications, including computer vision, natural language, speech, and translation. In this session, Siemens Financial Services (SFS) presents how it is using TensorFlow on Amazon SageMaker to develop machine learning models for investment due diligence. This application is focused on natural language processing, and it accelerates due diligence by extracting the most relevant and critical information from supporting documents. Both AWS and SFS share best practices for building and deploying TensorFlow models on AWS. Session Eric Kessler
Eric Anderson
AIM401-R2 - [REPEAT 2] Deep Learning Applications Using TensorFlow, ft. Advanced Microgrid Solutions The TensorFlow deep learning framework is used for developing diverse artificial intelligence (AI) applications, including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. Then, hear from Advanced Microgrid Solutions about how they implemented a deep neural network architecture with Keras and TensorFlow to forecast energy prices in near real time. Session Julien Simon
Andrew Martinez
Kevin Clifford
AIM401-R2OFM - [OVERFLOW] [REPEAT 2] Deep Learning Applications Using TensorFlow, ft. Advanced Microgrid Solutions (Teal-Mirage) The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Overflow
AIM401-R2OFV - [OVERFLOW] [REPEAT 2] Deep Learning Applications Using TensorFlow, ft. Advanced Microgrid Solutions (Teal-Venetian) The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Overflow
AIM401-ROFA - [OVERFLOW] [REPEAT] Deep Learning Applications Using TensorFlow (Green-Aria) The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Overflow
AIM401-ROFB - [OVERFLOW] [REPEAT] Deep Learning Applications Using TensorFlow (Green-Bellagio) The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Overflow
AIM401-ROFM - [OVERFLOW] [REPEAT] Deep Learning Applications Using TensorFlow (Green-Mirage) The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Overflow
AIM401-ROFMGM - [OVERFLOW] [REPEAT] Deep Learning Applications Using TensorFlow (Green-MGM) The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Overflow
AIM401-ROFV - [OVERFLOW] [REPEAT] Deep Learning Applications Using TensorFlow (Green-Venetian) The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow. Overflow
AIM402-R - [REPEAT] Deep Learning Applications Using PyTorch, Featuring Facebook With support for PyTorch 1.0 on Amazon SageMaker, you now have a flexible deep learning framework combined with a fully managed machine learning platform to transition seamlessly from research prototyping to production deployment. In this session, learn how to develop with PyTorch 1.0 within Amazon SageMaker using a novel generative adversarial network (GAN) tutorial. Then, hear from Facebook on how you can use the FAIRSeq modeling toolkit, which serves 6B translations daily for Facebook users, to train your own custom PyTorch models on Amazon SageMaker. Facebook also discusses the evolution of PyTorch 1.0 and features introduced to accelerate research and deployment. Session Dan Mbanga
Joe Spisak
AIM402-R1 - [REPEAT 1] Deep Learning Applications Using PyTorch, Featuring Facebook With support for PyTorch 1.0 on Amazon SageMaker, you now have a flexible deep learning framework combined with a fully managed machine learning platform to transition seamlessly from research prototyping to production deployment. In this session, learn how to develop with PyTorch 1.0 within Amazon SageMaker using a novel generative adversarial network (GAN) tutorial. Then, hear from Facebook on how you can use the FAIRSeq modeling toolkit, which serves 6B translations daily for Facebook users, to train your own custom PyTorch models on Amazon SageMaker. Facebook also discusses the evolution of PyTorch 1.0 and features introduced to accelerate research and deployment. Session Dan Mbanga
Jeff Smith
AIM403-R - [REPEAT] Integrate Amazon SageMaker with Apache Spark, ft. Moody's Amazon SageMaker, our fully managed machine learning platform, comes with pre-built algorithms and popular deep learning frameworks. Amazon SageMaker also includes an Apache Spark library that you can use to easily train models from your Spark clusters. In this code-level session, we show you how to integrate your Apache Spark application with Amazon SageMaker. We also dive deep into starting training jobs from Spark, integrating training jobs in Spark pipelines, and more. Session Keith Steward
Lauren Ottaviano Shukla
AIM403-R1 - [REPEAT 1] Integrate Amazon SageMaker with Apache Spark, ft. Moody's Amazon SageMaker, our fully managed machine learning platform, comes with pre-built algorithms and popular deep learning frameworks. Amazon SageMaker also includes an Apache Spark library that you can use to easily train models from your Spark clusters. In this code-level session, we show you how to integrate your Apache Spark application with Amazon SageMaker. We also dive deep into starting training jobs from Spark, integrating training jobs in Spark pipelines, and more. Session Keith Steward
Lauren Ottaviano Shukla
AIM404-R - [REPEAT] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. 21st Century Fox Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Session Leo Dirac
Lluis Canet
AIM404-R1 - [REPEAT 1] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. the NFL Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Session Andrea Olgiati
Matt Swensson
Michael Chi
AIM404-R2 - [REPEAT 2] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. Intuit Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Session David Arpin
Prasada Prabhu
AIM404-R2OFA - [OVERFLOW] [REPEAT 2] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. Intuit (Red-Aria) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-R2OFB - [OVERFLOW] [REPEAT 2] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. Intuit (Red-Bellagio) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-R2OFM - [OVERFLOW] [REPEAT 2] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. Intuit (Red-Mirage) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-R2OFMGM - [OVERFLOW] [REPEAT 2] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. Intuit (Red-MGM) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-R2OFV - [OVERFLOW] [REPEAT 2] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. Intuit (Red-Venetian) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-ROFA - [OVERFLOW] [REPEAT] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. 21st Century Fox (Green-Aria) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-ROFB - [OVERFLOW] [REPEAT] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. 21st Century Fox (Green-Bellagio) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-ROFM - [OVERFLOW] [REPEAT] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. 21st Century Fox (Green-Mirage) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-ROFMGM - [OVERFLOW] [REPEAT] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. 21st Century Fox (Green-MGM) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM404-ROFV - [OVERFLOW] [REPEAT] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. 21st Century Fox (Green-Venetian) Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Overflow
AIM405-R - [REPEAT] Better Analytics Through Natural Language Processing Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Session Nino Bice
Ben Snively
Dmytro Dolgopolov
Matt Cardillo
AIM405-R1 - [REPEAT 1] Better Analytics Through Natural Language Processing Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Session Nino Bice
Ben Snively
Sanjay Sharma
AIM405-ROFA - [OVERFLOW] [REPEAT] Better Analytics Through Natural Language Processing (Teal-Aria) Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Overflow
AIM405-ROFB - [OVERFLOW] [REPEAT] Better Analytics Through Natural Language Processing (Teal-Bellagio) Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Overflow
AIM405-ROFM - [OVERFLOW] [REPEAT] Better Analytics Through Natural Language Processing (Teal-Mirage) Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Overflow
AIM405-ROFMGM - [OVERFLOW] [REPEAT] Better Analytics Through Natural Language Processing (Teal-MGM) Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Overflow
AIM405-ROFN - [OVERFLOW] [REPEAT] Better Analytics Through Natural Language Processing (3-Nuvola) Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Overflow
AIM405-ROFS - [OVERFLOW] [REPEAT] Better Analytics Through Natural Language Processing (3-Scamall) Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Overflow
AIM405-ROFV - [OVERFLOW] [REPEAT] Better Analytics Through Natural Language Processing (Teal-Venetian) Natural language processing holds the key to unlocking business value from unstructured data. Organizations that implement effective data analysis methods gain a competitive advantage through improved decision-making, risk reduction, or enhanced customer experience. In this session, learn how to easily process, analyze, and visualize data by pairing Amazon Comprehend with services like Amazon Relational Database Service (Amazon RDS), Amazon Elasticsearch Service, and Amazon Neptune. We also share real-world examples of how customers built text analytics solutions with Amazon Comprehend. Overflow
AIM406 - Unlock the Full Potential of Your Media Assets, ft. Fox Entertainment Group Machine learning (ML) enables developers to build scalable solutions that maximizes the use of media assets through automatic metadata extraction. From automatic transcription and language translation to face detection and celebrity recognition, ML enables you to automate manual workflows and optimize the use of your video content. In this session, learn how to use services such as Amazon Rekognition, Amazon Translate, and Amazon Comprehend to build a searchable video library, automate the creation of highlight reels, and more. Session Chris Kuthan
Venkatesh Bagaria
Michael Bacharach
AIM407-R - [REPEAT] Build Deep Learning Applications Using Apache MXNet, Featuring Workday The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, and natural language processing at scale. In this session, learn how to get started with MXNet on the Amazon SageMaker machine learning platform. Hear from Workday about how they built computer vision and natural language processing (NLP) models using MXNet to automatically extract information from paper documents, such as expense receipts and populate data records. Workday also shares its experience using Sockeye, an MXNet toolkit for quickly prototyping sequence-to-sequence NLP models. Session Cyrus Vahid
Vivek Srivastava
Yunxing (Henry) Zhang
AIM407-R1 - [REPEAT 1] Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, natural language processing, and more at scale. In this session, learn how to get started with Apache MXNet on the Amazon SageMaker machine learning platform. Chick-fil-A share how they got started with MXNet on Amazon SageMaker to measure waffle fry freshness and how they leverage AWS services to improve the Chick-fil-A guest experience. Session Cyrus Vahid
Jay Duff
AIM409 - Build a "Who's Who" App for Your Media Content Video has become an increasingly successful medium for advertising, marketing, and engaging customers. However, many companies underutilize their substantial video assets because they are poorly indexed and cataloged. In this workshop, learn how to use machine learning services to gain more value from video by building a customer celebrity detection feature that can recognize mainstream celebrities and individuals from your own uploaded media files. Workshop Kashif Imran
AIM410-R - [REPEAT] Build, Train, and Deploy ML Models with Amazon SageMaker Come and help build the most accurate text classification model possible. A fully managed machine learning (ML) platform, Amazon SageMaker enables developers and data scientists to build, train, and deploy ML models using built-in or custom algorithms. In this workshop, you learn how to leverage Keras/TensorFlow deep learning frameworks to build a text classification solution using custom algorithms on Amazon SageMaker. You package custom training code in a Docker container, test it locally, and then use Amazon SageMaker to train a deep learning model. You then try to iteratively improve the model to achieve a higher level of accuracy. Finally, you deploy the model in production so different applications within the company can start leveraging this ML classification service. Please note that to actively participate in this workshop, you need an active AWS account with admin-level IAM permissions to Amazon SageMaker, Amazon Elastic Container Registry (Amazon ECR), and Amazon S3. Workshop Ahmad Khan
AIM410-R1 - [REPEAT 1] Build, Train, and Deploy ML Models with Amazon SageMaker Come and help build the most accurate text classification model possible. A fully managed machine learning (ML) platform, Amazon SageMaker enables developers and data scientists to build, train, and deploy ML models using built-in or custom algorithms. In this workshop, you learn how to leverage Keras/TensorFlow deep learning frameworks to build a text classification solution using custom algorithms on Amazon SageMaker. You package custom training code in a Docker container, test it locally, and then use Amazon SageMaker to train a deep learning model. You then try to iteratively improve the model to achieve a higher level of accuracy. Finally, you deploy the model in production so different applications within the company can start leveraging this ML classification service. Please note that to actively participate in this workshop, you need an active AWS account with admin-level IAM permissions to Amazon SageMaker, Amazon Elastic Container Registry (Amazon ECR), and Amazon S3. Workshop Ahmad Khan
AIM410-R2 - [REPEAT 2] Build, Train, and Deploy ML Models with Amazon SageMaker Come and help build the most accurate text classification model possible. A fully managed machine learning (ML) platform, Amazon SageMaker enables developers and data scientists to build, train, and deploy ML models using built-in or custom algorithms. In this workshop, you learn how to leverage Keras/TensorFlow deep learning frameworks to build a text classification solution using custom algorithms on Amazon SageMaker. You package custom training code in a Docker container, test it locally, and then use Amazon SageMaker to train a deep learning model. You then try to iteratively improve the model to achieve a higher level of accuracy. Finally, you deploy the model in production so different applications within the company can start leveraging this ML classification service. Please note that to actively participate in this workshop, you need an active AWS account with admin-level IAM permissions to Amazon SageMaker, Amazon Elastic Container Registry (Amazon ECR), and Amazon S3. Workshop Ahmad Khan
AIM411 - Uber on Using Horovod for Distributed Deep Learning One of the main challenges customers face is running efficient deep learning training over multiple nodes. In this chalk talk, Uber discusses how to use Horovod, a distributed training framework, to speed up deep learning training on TensorFlow and PyTorch. Chalk Talk Gitansh Chadha
Alex Sergeev
AIM412 - Automatic Model Tuning Using Amazon SageMaker In many cases, what separates good models from great ones is the choice of hyperparameters. For example, what is the number of layers you should use; what should be the learning rate; what should be regularization parameters, and so on. In this session, learn how Amazon SageMaker makes discovering the best set of hyperparameters an informed process during training. Chalk Talk Cyrus Vahid
AIM413 - Deploying Your ONNX Deep Learning with Apache MXNet Model Server In this chalk talk, we discuss how you can use Apache MXNet Model Server to deploy ONNX models. We get into the nuts and bolts of deployments, and we discuss monitoring model performance using Amazon CloudWatch integration. Chalk Talk Steffen Rochel
Girish Dilip Patil
AIM414 - Sequence-to-Sequence Modeling with Apache MXNet, Sockeye, and Amazon SageMaker In this session, we discuss the "encoder-decoder architecture with attention," a state-of-the-art architecture for natural language processing. This architecture is implemented in the Sockeye package of MXNet and is used by the sequence-to-sequence algorithm of Amazon SageMaker. Chalk Talk Orchid Majumder
Girish Dilip Patil
AIM415-R - [REPEAT] Capture Voice of Customer Insights with NLP & Analytics Understanding your customers is easier today than ever before. Natural language capabilities can capture a wealth of information, such as user sentiment and conversational intent. This workshop teaches you how to build an analytics pipeline that includes natural language processing (NLP) to better understand how to improve the customer experience. Attendees learn how to use AWS services, including Amazon Comprehend and Amazon Transcribe, to process and perform analysis on customer data, such as contact center call recordings. Workshop Atul Deo
Yasser El-Haggan
AIM415-R1 - [REPEAT 1] Capture Voice of Customer Insights with NLP & Analytics Understanding your customers is easier today than ever before. Natural language capabilities can capture a wealth of information, such as user sentiment and conversational intent. This workshop teaches you how to build an analytics pipeline that includes natural language processing (NLP) to better understand how to improve the customer experience. Attendees learn how to use AWS services, including Amazon Comprehend and Amazon Transcribe, to process and perform analysis on customer data, such as contact center call recordings. Workshop Yasser El-Haggan
Atul Deo
Ben Snively
AIM416 - Build an ETL Pipeline to Analyze Customer Data Consumers today freely express their satisfaction or frustration with a company or product online through social media, blogs, and review platforms. Sentiment analysis can help companies better understand their customers' opinions and needs, and make more informed business decisions. In this workshop, learn how to use Amazon Comprehend to analyze sentiment. Also learn how to build a serverless data processing pipeline that consumes raw Amazon product reviews from Amazon S3, cleans the dataset, extracts sentiment from each review, and writes the output back to Amazon S3. Workshop Jean-Pierre Dodel
Roy Hasson
AIM417 - Build a Searchable Media Library & Moderate Content at Scale Using Machine Learning Companies have to process, analyze, and extract meaning from ever-growing volumes of audio, image, and video data. Automating media workflows, such as image and video indexing or manual transcription for closed captions, can help you scale the growth of your media library and save time from manual, error-prone work. In this workshop, you learn how to automate workflows using the Media Analysis Solution, which includes Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. You learn how to extract metadata from media files and create a searchable library of metadata. Workshop Kashif Imran
AIM418 - Build Deep Learning Applications Using MXNet and Amazon SageMaker In this workshop, learn how to get started with the Apache MXNet deep learning framework using Amazon SageMaker, a fully managed platform to build, train, and deploy machine learning models at scale quickly and easily. Learn how to build a model using MXNet for a computer vision use case. Once the model is built, learn how to quickly train it to get the best possible results and then easily deploy it to production using Amazon SageMaker. Workshop Cyrus Vahid
AIM419 - Train Models on Amazon SageMaker Using Data Not from Amazon S3 Questions often arise about training machine learning models using Amazon SageMaker with data from sources other than Amazon S3. In this chalk talk, we dive deep into training models in real time using data from Amazon DynamoDB or a relational database. We demonstrate how training models with Amazon SageMaker is quick and easy, regardless of the data source. Chalk Talk Keith Steward
AIM420 - Detect Anomalies Using Amazon SageMaker For a wide variety of metrics—including business metrics, application metrics, and low-level software and hardware metrics—it is critical to detect abnormalities to ensure that you end up with the right data. In this chalk talk, learn about the Random Cut Forest algorithm built into Amazon SageMaker in order to detect anomalies. We dive deep into detecting anomalies and tuning data in order to find practical solutions. Chalk Talk Denis V. Batalov
AIM421 - Build a Custom Model for Object & Logo Detection Detecting specific objects and logos is a feature that can help companies in any industry, from media and entertainment to financial services. However, detecting new objects or logos requires building a custom model. In this chalk talk, learn how to use Amazon Rekognition and Amazon SageMaker to build a custom model to detect logos, objects, or even inappropriate content. Chalk Talk Liam Morrison
Kris Skrinak
AIM422 - Fraud Detection and Prevention Using Amazon SageMaker and Amazon Neptune Business fraud is a growing concern across online and offline transactions. In this chalk talk, we dive into detecting fraud using machine learning with Amazon SageMaker and Amazon Neptune. We discuss the details of building models, such as class imbalance. We also discuss the different costs of false positives and false negatives. Additionally, we talk about algorithms like Linear Learners that can be used to build healthy models in such scenarios. Chalk Talk Ekta Parashar
Abhishek Mishra
AIM423 - Debugging Gluon and Apache MXNet In this chalk talk, we discuss how to troubleshoot the Gluon API for Apache MXNet from a PyCharm development environment by connecting to a remote server. We also discuss how to visualize the model and performance data using MXBoard. Chalk Talk Thomas Delteil
Girish Dilip Patil
AIM428 - Building, Training, and Deploying fast.ai Models Using Amazon SageMaker In a short space of time, fast.ai has become a popular Deep Learning library, driven by the success of the fast.ai online Massive Open Online Course (MOOC). It has allowed SW developers to achieve, in the span of a few weeks, state-of-the-art results in domains such as Computer Vision (CV), Natural Language Processing (NLP), and structured data machine learning. In this chalk talk, we go into the details of building, training, and deploying fast.ai-based models using Amazon SageMaker. Chalk Talk Matthew McClean
Andrew Shaw
AIM429-R - [REPEAT] Build Deep Learning Applications Using TensorFlow and Amazon SageMaker Deep learning continues to push the state of the art in computer vision, language applications, and more. In this workshop, learn how to get started with the TensorFlow deep learning framework using Amazon SageMaker, a fully managed platform to build, train, and deploy machine learning models at scale. Learn how to build a model using TensorFlow by setting up a Jupyter notebook for image and object recognition. Use bring-your-own-code and bring-your-own-algorithm techniques to develop your deep learning model. Once the model is built, learn how to train and deploy it using Amazon SageMaker. Workshop Amit Sharma
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