No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
There aren't any available sessions at this time.
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
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
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
Uppercase Letter
Lowercase Letter
Uppercase or Lowercase Letter
Number
Special Character
Password length of
or more and have
of the following:
Password 2 does not match password.
AWS Summit New York 2018
Add to My Interests
Remove from My Interests

SRV336 - Train Machine Learning Models Using Amazon SageMaker with TensorFlow

Session Description

Amazon SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy machine learning (ML) models in production applications easily and at scale. In this chalk talk, we dive deep into training an ML model based on the TensorFlow framework. We discuss the specifics of training a model through Amazon SageMaker by taking an algorithm and running it on a training cluster in an auto-scaling group. This session showcases the scalability of training that is possible with Amazon SageMaker, which reduces the time and cost of training runs.


Session Schedule
    Session Speakers
    Additional Information
    Chalk Talk
    Do Not Sell My Personal Information
    First name
    Last name
    Email address