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These new features simplify and speed up ML feature management and governance.
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Amazon SageMaker Feature Store has introduced three new capabilities in its Python SDK v3.8.0. These updates include enhanced support for Lake Formation governance and Iceberg table properties. The improvements aim to accelerate machine learning feature pipelines with practical code examples and notebooks.

These new features simplify and speed up ML feature management and governance.
Amazon (AMZN)
Faster, governed feature pipelines can improve ML model deployment efficiency and compliance.
ML teams using SageMaker should evaluate these updates to optimize their workflows.
Sources in this thread (1): AWS Machine Learning Blog
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Amazon SageMaker Feature Store has introduced three new capabilities in its Python SDK v3.8.0. These updates include enhanced support for Lake Formation governance and Iceberg table properties. The improvements aim to accelerate machine learning feature pipelines with practical code examples and notebooks.
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Amazon SageMaker Feature Store has introduced three new capabilities in its Python SDK v3.8.0. These updates include enhanced support for Lake Formation governance and Iceberg table properties. The improvements aim to accelerate machine learning feature pipelines with practical code examples and notebooks.