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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. 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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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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Automatically scale Amazon SageMaker endpoints for inference

In this session, learn how to use Amazon SageMaker to reduce costs for intermittent workloads and scale aut...

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

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