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Implementing MLOps practices with Amazon SageMaker

MLOps practices help data scientists and IT operations professionals collaborate and manage the production ML workflow, including data preparation and building, training, deploying, and monitoring models.

During this session, explore the features in Amazon SageMaker Pipelines that help you increase automation, track data lineage, catalog ML models for production, improve the quality of your end-to-end workflows, and support governance.

Also, learn how to use SageMaker projects, which provide MLOps templates for incorporating CI/CD practices into your ML pipelines.

Click here and download the presentation deck to learn more!

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Train deep learning models faster with Amazon SageMaker

In this session, you’ll learn about compiler technologies such as operator fusion and how SageMaker Trainin...

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Achieve high performance and cost-effective model deployment

Explore the different inference options available in Amazon SageMaker such as multi-container endpoints, in...

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Implementing MLOps practices with Amazon SageMaker

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