Confirm and Proceed
View More
View Less
System Message
An unknown error has occurred and your request could not be completed. Please contact support.
Reserved - Scan in at least 10 minutes before the beginning of the session.
This has been added to your Planner. Please note: This is not a reserved seat.
Waitlisted - You may be assigned a reserved seat if one becomes available.

Please be sure to check the session schedule for any repeats of this session. In order to search for repeats of this session, please type the Session ID into the search bar at the top of the page.
Personal Calendar
Conference Event
There aren't any available sessions at this time.
Conflict Found
This session is already scheduled at another time. Would you like to...
Please enter a maximum of {0} characters.
{0} remaining of {1} character maximum.
Please enter a maximum of {0} words.
{0} remaining of {1} word maximum.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Replies ()
New Post
Microblog Thread
Post Reply
Your session timed out.
Meeting Summary

ADT302 - Democratize Data Preparation for Analytics & Machine Learning: A Hands-On Lab

Session Description

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.

Session Speakers
Additional Information
Builders Session
300 - Advanced
Please note that session information is subject to change.
Session Schedule