AWS SageMaker: New Tools for Machine Learning
AWS has announced three improvements to SageMaker Feature Store: Lake Formation for data access control, Iceberg support for better scalability, and optimization of machine learning pipelines. The new capabilities ship in Python SDK v3.8.0.
AI-processed from AWS Machine Learning Blog; edited by Hamidun News
AWS has added three new capabilities to speed up machine learning in SageMaker Feature Store, available in Python SDK v3.8.0.
What is AWS
SageMaker Feature Store and why does machine learning need it? It is a specialized service for managing ML features: a store from which models pull data for training and inference. Features have to be transformed, versioned and synchronized across models, so you cannot simply use S3.
What new capabilities has AWS added to SageMaker?
Three updates in Python SDK v3.8.0, aimed at speeding up and simplifying the creation of ML pipelines, especially for teams that manage large volumes of features.
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Key Questions Answered
What is AWS SageMaker Feature Store and why does machine learning need it?
It is a specialized service for managing ML features: a store from which models pull data for training and inference. Features have to be transformed, versioned and synchronized across models, so you cannot simply use S3.
What new capabilities has AWS added to SageMaker?
Three updates in Python SDK v3.8.0, aimed at speeding up and simplifying the creation of ML pipelines, especially for teams that manage large volumes of features.
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