Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store
Quick Answer
Amazon SageMaker Feature Store introduces three new capabilities in the SageMaker Python SDK v3.8.0, enhancing ML feature pipelines.
Quick Take
Amazon SageMaker Feature Store introduces three new capabilities in the SageMaker Python SDK v3.8.0, enhancing ML feature pipelines. The update includes code examples and notebooks for Lake Formation governance and Iceberg table properties, streamlining the development process for data scientists and ML engineers.
Key Points
- New capabilities enhance the efficiency of ML feature pipelines in SageMaker.
- Includes code examples for easy implementation by developers.
- Notebooks available for Lake Formation governance and Iceberg table properties.
- Targets data scientists and ML engineers for improved workflow.
- Version 3.8.0 of the SageMaker Python SDK is now available.
Article Excerpt
From source RSS / original summaryToday, 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 properties in the SageMaker Python SDK repository.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from AWS Machine Learning
See more →
Best practices for multi-turn reinforcement learning in Amazon SageMaker AI
This article outlines best practices for multi-turn reinforcement learning (RL) training in Amazon SageMaker. Key strategies include establishing a reliable training environment, implementing external evaluations, designing task-aligned rewards, managing agent behavior over multiple turns, and monitoring performance metrics to guide iterative improvements.
