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Build custom deep learning environments with AWS Deep Learning Containers

Deep learning (DL) projects often require integrating custom libraries with popular open-source frameworks such as TensorFlow, PyTorch, and Hugging Face. Setting up, managing, and scaling custom ML environments can be time consuming and cumbersome, even for experts. With AWS Deep Learning Containers, you get access to prepackaged and optimized DL frameworks that make it easy for you to customize, extend, and scale your environments. In this session, learn how to use Deep Learning Containers to build your custom ML environment and how to implement model training and inference with Deep Learning Containers in Amazon SageMaker.

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Choosing the right ML instances for your training and inference deployments

In this session, explore how to choose the proper instance for ML training and inference based on model siz...

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ML at the edge with Amazon SageMaker

In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive mainten...

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Build custom deep learning environments with AWS Deep Learning Containers

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