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ML at the edge with Amazon SageMaker

More ML models are being deployed on edge devices, such as robots and smart cameras. In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more. Learn how to operate and monitor multiple models across a fleet of devices. Also walk through the process to build and train CV models with Amazon SageMaker and how to package, deploy, and manage them with SageMaker Edge Manager. The chalk talk also covers edge device setup and MLOps lifecycle with over-the-air model updates and data capture to the cloud.

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Build custom deep learning environments with AWS Deep Learning Containers

In this session, learn how to use Deep Learning Containers to build your custom ML environment and how to i...

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DJL: An open-source library to build and deploy deep learning in Java

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning.

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ML at the edge with Amazon SageMaker

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