Broadcast Date: August 26, 2019
Level: 300
Data scientists and machine learning engineers use containers to create custom, lightweight environments to train and serve models at scale with deep learning frameworks such as TensorFlow, Apache MXNet, and PyTorch. With containers, developers get consistent environments for development and deployment. In this tech talk, we'll show you how to use AWS Deep Learning Containers to train and serve models at scale with Amazon SageMaker.
Learning Objectives
- Learn about machine learning using containers and Amazon SageMaker
- Learn about how to use AWS Learning Containers to create custom, lightweight machine learning environments, along with Amazon SageMaker
- Learn to train and deploy machine learning models at scale
Who Should Attend?
Machine Learning Practitioners, Developers, Data Scientists, Technical Decision Makers, Architects
Speakers
- Shashank Prasanna, Sr Technical Evangelist, AI/ML, AWS
Learn More
To learn more about the services featured in this talk, please visit:
https://aws.amazon.com/sagemaker
Intro body copy here about 2018 re:Invent launches.
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