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Achieve high-performance and cost-effective model deployment

High-performance and cost-effective techniques, including real-time, asynchronous, and batch, are needed to scale model deployments to maximize your ML investments. In this session, learn the different inference options available in Amazon SageMaker, such as multi-container endpoints, inference pipelines, and multi-model endpoints as well as frameworks such as TensorFlow and PyTorch, Python-based backend servers, and C++/Go-based backend servers. Learn how to pick the best inference option for your ML use case so you can scale to thousands of models across your business.

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Use Amazon SageMaker to build high-quality ML models faster
Use Amazon SageMaker to build high-quality ML models faster

Amazon SageMaker provides all the tools and libraries you need to build ML models.

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Implementing MLOps practices with Amazon SageMaker
Implementing MLOps practices with Amazon SageMaker

MLOps practices help data scientists & IT operations professionals collaborate & manage the production ML w...

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