Amazon Web Services

Today, Amazon SageMaker launches two new capabilities designed to simplify data management and governance. The first capability, automated lakehouse onboarding, allows customers to automatically ingest metadata for datasets, such as Glue Data Catalog tables, into SageMaker Catalog. This occurs when…

You can now add Amazon S3 general purpose buckets to the Amazon SageMaker Catalog. This helps data scientists, engineers, and business analysts to easily discover and access datasets in Amazon S3, while giving data producers the ability to maintain granular…

Amazon SageMaker now offers a visual builder experience for creating and managing workflows. This feature is part of the next generation of Amazon SageMaker - the center for all your data, analytics, and AI, and is available within SageMaker Unified…

Amazon S3 Vectors delivers purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. By reducing the cost of uploading, storing, and querying vectors by up to 90%, S3 Vectors makes…

Amazon Redshift now supports automatic refresh of materialized views that are defined on external Apache Iceberg tables in the Amazon S3 data lake. With this update, Amazon Redshift will automatically refresh a materialized view defined on Apache Iceberg tables, that…

TwelveLabs' Marengo 2.7 and Pegasus 1.2 multimodal foundation models are now available in Amazon Bedrock. Marengo 2.7 is a video embedding model proficient at performing tasks such as search and classification, enabling enhanced video understanding, while Pegasus 1.2 is a…