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. It provides a next-generation model serving solution that makes it easy to deploy deep learning models to production. DJL supports popular deep learning engines and frameworks such as PyTorch, TensorFlow, ONNX Runtime, and Apache MXNet. In this session, get an overview of DJL and dive deep on how DJL is designed and works in different use cases. Also learn how to tune DJL for performance and how to use DJL for serving large workloads in production.

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Solve common business use cases with Amazon Neptune ML
Solve common business use cases with Amazon Neptune ML

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Choosing the right ML instances for your training and inference deployments
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...