AWS Management & Governance Blog

Category: Amazon SageMaker

Using AWS CloudTrail to propagate tags across related AWS resources - Part 2

Using AWS CloudTrail to propagate tags across related AWS resources – Part 2

AWS allows customers to assign metadata to their AWS resources in the form of tags. Each tag consists of a customer-defined key and an optional value. Tags can make it easier to manage, search for, and filter resources by purpose, owner, environment, or other criteria. AWS tags can be used for many purposes like organizing […]

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Setting up secure, well-governed machine learning environments on AWS.

Setting up secure, well-governed machine learning environments on AWS

When customers begin their machine learning (ML) journey, it’s common for individual teams in a line of business (LoB) to set up their own ML environments. This provides teams with flexibility in their tooling choices, so they can move fast to meet business objectives. However, a key difference between ML projects and other IT projects is […]

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Building secure Amazon SageMaker access URLs with AWS Service Catalog

Many customers need a secure method to access Amazon SageMaker notebooks within their private network without logging in to the AWS console, or using the AWS CLI/SDKs. This may be desired for enhanced security or to provide an easier self-service path for data scientists. In this blog post, we show you a how to connect […]

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Enable self-service, secured data science using Amazon SageMaker notebooks and AWS Service Catalog

by Sanjay Garje and Vebhhav (Veb) Singh Enterprises of all sizes are moving to the AWS Cloud. We hear from leadership of those enterprise teams that they are looking to provide a safe, cost-governed way to provide easy access to Amazon SageMaker to promote experimentation with data science to unlock new business opportunities and disrupt […]

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