Unlocking Greater Flexibility: Amazon SageMaker Catalog Now Supports Amazon S3 General Purpose Buckets
Seattle, WA – July 15, 2025 – Amazon Web Services (AWS) is pleased to announce a significant enhancement to Amazon SageMaker Catalog, its feature designed to help users discover and manage machine learning assets. Effective today, SageMaker Catalog now seamlessly integrates with and supports Amazon S3 general purpose buckets. This update represents a valuable step forward, offering customers greater flexibility and simplifying the process of organizing and accessing their machine learning data and models within the AWS ecosystem.
Previously, SageMaker Catalog’s integration with Amazon S3 was primarily focused on specific bucket types. With this latest advancement, users can now leverage their existing general purpose S3 buckets to store and manage a wider array of their machine learning artifacts. This includes datasets, model artifacts, code, and other crucial components of the machine learning lifecycle.
The ability to utilize general purpose S3 buckets provides several key benefits for Amazon SageMaker users:
- Enhanced Flexibility and Convenience: Customers can now consolidate their machine learning assets within their established S3 storage infrastructure, eliminating the need to create and manage separate bucket configurations solely for SageMaker Catalog. This streamlines data management and reduces operational overhead.
- Simplified Asset Discovery and Management: By bringing all relevant machine learning assets into a unified catalog, users can more easily discover, version, and manage their datasets, models, and other resources. This fosters better collaboration and accelerates the development process.
- Leveraging Existing Infrastructure: Organizations that have already invested in and configured general purpose S3 buckets for their data lakes or other storage needs can now seamlessly integrate these existing resources with SageMaker Catalog, maximizing their existing AWS investments.
- Cost Optimization: For many users, general purpose S3 buckets offer a cost-effective storage solution. This integration allows them to harness the capabilities of SageMaker Catalog without incurring additional costs associated with specialized storage configurations.
- Broader Accessibility: This enhancement ensures that a wider range of users, from individual data scientists to large enterprise teams, can benefit from SageMaker Catalog’s robust features, regardless of their current S3 bucket configurations.
“We are continuously striving to make Amazon SageMaker more accessible and user-friendly for our customers,” said [Name and Title of AWS Spokesperson – Placeholder]. “The addition of support for Amazon S3 general purpose buckets is a direct response to customer feedback and reflects our commitment to providing flexible and powerful tools for machine learning. This integration will empower our users to manage their ML assets more efficiently and accelerate their journey from experimentation to production.”
This update underscores AWS’s dedication to simplifying and optimizing the machine learning workflow. By enabling SageMaker Catalog to work with general purpose S3 buckets, AWS is empowering developers and data scientists to focus more on building innovative ML solutions and less on the complexities of infrastructure management.
Customers can begin leveraging this new functionality immediately by configuring their SageMaker Catalog to reference their existing Amazon S3 general purpose buckets. AWS encourages users to explore this enhancement and discover how it can further streamline their machine learning operations.
Amazon SageMaker Catalog adds support for Amazon S3 general purpose buckets
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Amazon published ‘Amazon SageMaker Catalog adds support for Amazon S3 general purpose buckets’ at 2025-07-15 22:35. Please write a detailed article about this news in a polite tone with relevant information. Please reply in English with the article only.