Author: Ramesh H Singh

If you’re struggling with manual data classification in your organization, the new Amazon SageMaker Catalog AI agent can automate this process for you. Most large organizations face challenges with the manual tagging of data assets, which doesn’t scale and is unreliable. In some cases, business terms aren’t applied consistently across teams. Different groups name and tag data assets based on local conventions. This creates a fragmented catalog where discovery becomes unreliable and governance teams spend more time normalizing metadata than governing. In this post, we show you how to implement this automated classification to help reduce the manual tagging effort and improve metadata consistency across your organization.

Finding the right data assets in large enterprise catalogs can be challenging, especially when thousands of datasets are cataloged with organization-specific metadata. Amazon SageMaker Unified Studio now supports custom metadata search filters. In this post, you learn how to create custom metadata forms, publish assets with metadata values, and use structured filters to discover those assets.

Amazon SageMaker Catalog now supports metadata enforcement rules for glossary terms classification (tagging) at the asset level. With this capability, administrators can require that assets include specific business terms or classifications. Data producers must apply required glossary terms or classifications before an asset can be published. In this post, we show how to enforce business glossary classification rules in SageMaker Catalog.

Amazon SageMaker Catalog now supports custom metadata forms and rich text descriptions at the column level, extending existing curation capabilities for business names, descriptions, and glossary term classifications. Column-level context is essential for understanding and trusting data. This release helps organizations improve data discoverability, collaboration, and governance by letting metadata stewards document columns using structured and formatted information that aligns with internal standards. In this post, we show how to enhance data discovery in SageMaker Catalog with custom metadata forms and rich text documentation at the schema level.

Amazon SageMaker now enhances search results in Amazon SageMaker Unified Studio with additional context that improves transparency and interpretability. The capability introduces inline highlighting for matched terms and an explanation panel that details where and how each match occurred across metadata fields such as name, description, glossary, and schema. In this post, we demonstrate how to use enhanced search in Amazon SageMaker.

Security and compliance concerns are key considerations when customers across industries rely on Amazon SageMaker Catalog. Customers use SageMaker Catalog to organize, discover, and govern data and machine learning (ML) assets. A common request from domain administrators is the ability to enforce governance controls on certain metadata terms that carry compliance or policy significance. Examples […]

Amazon SageMaker now supports account-agnostic project profiles, so you can create reusable project templates across multiple AWS accounts and organizational units. In this post, we demonstrate how account-agnostic project profiles can help you simplify and streamline the management of SageMaker project creation while maintaining security and governance features. We walk through the technical steps to configure account-agnostic, reusable project profiles, helping you maximize the flexibility of your SageMaker deployments.

Amazon SageMaker Catalog now supports generative AI-powered recommendations for business descriptions, including table summaries, use cases, and column-level descriptions for custom structured assets registered programmatically. In this post, we demonstrate how to generate AI recommendations for business descriptions for custom structured assets in SageMaker Catalog.

We’re excited to introduce a new enhancement to the search experience in Amazon SageMaker Catalog, part of the next generation of Amazon SageMaker—exact match search using technical identifiers. In this post, we demonstrate how to streamline data discovery with precise technical identifier search in Amazon SageMaker Unified Studio.

We’re excited to announce a new feature in Amazon DataZone that offers enhanced metadata governance for your subscription approval process. Using this update, domain owners can define metadata requirements and enforce them on data consumers when they request subscriptions to data assets. By making it mandatory for data consumers to provide specific metadata, domain owners can achieve compliance, meet organizational standards, and support audit and reporting needs.