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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points

AWS Machine Learning

To develop models for such use cases, data scientists need access to various datasets like credit decision engines, customer transactions, risk appetite, and stress testing. Amazon S3 Access Points simplify managing and securing data access at scale for applications using shared datasets on Amazon S3.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise.

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning

This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process. One aspect of this data preparation is feature engineering. However, generalizing feature engineering is challenging.

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Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. Solution overview.

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Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

As data is growing at an exponential rate, organizations are looking to set up an integrated, cost-effective, and performant data platform in order to preprocess data, perform feature engineering, and build, train, and operationalize ML models at scale. In this post, we demonstrate how to implement this solution.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. The diagram shows several accounts and personas as part of the overall infrastructure. The following diagram gives a high-level illustration of the use case.