AWS Machine Learning Blog

Category: AWS Lake Formation

Control access to Amazon SageMaker Feature Store offline using AWS Lake Formation

You can establish feature stores to provide a central repository for machine learning (ML) features that can be shared with data science teams across your organization for training, batch scoring, and real-time inference. Data science teams can reuse features stored in the central repository, avoiding the need to reengineer feature pipelines for different projects and […]

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Enable cross-account access for Amazon SageMaker Data Wrangler using AWS Lake Formation

Amazon SageMaker Data Wrangler is the fastest and easiest way for data scientists to prepare data for machine learning (ML) applications. With Data Wrangler, you can simplify the process of feature engineering and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization through a single visual interface. Data Wrangler […]

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For an existing data lake registered with Lake Formation, the following diagram illustrates the proposed implementation.

Control and audit data exploration activities with Amazon SageMaker Studio and AWS Lake Formation

Certain industries are required to audit all access to their data. This includes auditing exploratory activities performed by data scientists, who usually query data from within machine learning (ML) notebooks. This post walks you through the steps to implement access control and auditing capabilities on a per-user basis, using Amazon SageMaker Studio notebooks and AWS […]

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