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Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

AWS just shipped three new Feature Store capabilities. Here's why your ML pipeline just got faster.

Reporting by AWS Machine Learning Blog 1 min brief
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The KeyNews take

Why it matters

Amazon SageMaker Feature Store rolls out developer tooling upgrades (Python SDK v3.8.0) that reduce friction in ML feature pipeline development—a infrastructure-layer play that matters for teams scaling production ML workloads.

The key facts

6 to know

  1. SageMaker Python SDK v3.8.0 released

  2. Three new capabilities announced

  3. Lake Formation governance integration

  4. Iceberg table properties support

  5. Code examples and notebooks provided

  6. Feature pipeline acceleration focus

Go to the source

AWS Machine Learning Blogaws.amazon.com

Publisher excerpt: Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance and Iceberg table…
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