![](https://play.vidyard.com/NH4rygQSJCNzMGMgpqGQSs.jpg)
Broadcast Date: May 27, 2022
Level: 200
State-of-the-art deep learning models can be difficult to train because of the cost, time, and skills required to optimize memory and compute. Join us for a discussion on how to overcome the common pitfalls of training deep learning models, and how Amazon SageMaker helps overcome these challenges by optimizing clusters of compute instances and more.
![](https://pages.awscloud.com/rs/112-TZM-766/images/learning-objective.png)
Learning Objectives
- Discover how to accelerate training of large computer vision and NLP models
- Learn how to get started quickly using Amazon SageMaker distributed training libraries and training compiler
- Find out about tutorials and technical resources to get hands-on experience and dive deeper
![](https://pages.awscloud.com/rs/112-TZM-766/images/who-attend.png)
Who Should Attend?
Data scientist, ML expert practitioners
Speakers
- Mani Khanuja - Sr. AI/ML Specialist SA
- Emily Webber - ML Specialist SA
![](https://pages.awscloud.com/rs/112-TZM-766/images/speakers.png)
Learn More
To learn more about the services featured in this talk, please visit:
https://aws.amazon.com/sagemaker/train
Intro body copy here about 2018 re:Invent launches.
Download the Slide Deck
Compute
Service How To
December 19th, 2018 | 1:00 PM PT
Developing Deep Learning Models for Computer Vision with
Amazon EC2 P3 Instances.
Data Lakes & Analytics
Webinar 1:
What's New / Cloud Innovation
December 10th, 2018 | 11:00 AM PT
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