Amazon SageMaker Unified Studio introduces new one-click onboarding experiences and serverless notebooks with a built-in AI agent without any manual set up or provisioning of your domain or compute resources. You can launch SageMaker Unified Studio directly from Amazon SageMaker, Amazon Athena, Amazon Redshift, and Amazon S3 Tables console pages, giving a fast path to analytics and AI workloads.

Amazon SageMaker Catalog now offers column-level metadata forms and enforced glossary requirements, enabling organizations to improve data classification, discoverability, and governance through standardized business metadata.

Amazon SageMaker has introduced three new capabilities—Amazon QuickSight integration for dashboard creation, governance, and sharing, Amazon S3 Unstructured Data Integration for cataloging documents and media files, and automatic data onboarding from Lakehouse—that eliminate data silos by unifying structured and unstructured data management, visualization, and governance in a single experience.

Amazon S3 Vectors is a new cloud object store that provides native support for storing and querying vectors at massive scale, offering up to 90% cost reduction compared to conventional approaches while seamlessly integrating with Amazon Bedrock Knowledge Bases, SageMaker, and OpenSearch for AI applications.

Simplify AI experimentation with zero-infrastructure MLflow that launches in minutes, scales automatically, and seamlessly integrates with SageMaker's model customization and pipeline capabilities.

Simplify AI experimentation with zero-infrastructure MLflow that launches in minutes, scales automatically, and seamlessly integrates with SageMaker's model customization and pipeline capabilities.

AWS AI league is a program that helps organizations upskill their workforce by combining fun competition with hands-on learning using AWS AI services. It offers a unique opportunity for both enterprises and developers to gain valuable and practical skills in fine-tuning, model customization, and prompt engineering - essential skills for building generative AI solutions.

Amazon SageMaker has introduced three new capabilities—Amazon QuickSight integration for dashboard creation, governance, and sharing, Amazon S3 Unstructured Data Integration for cataloging documents and media files, and automatic data onboarding from Lakehouse—that eliminate data silos by unifying structured and unstructured data management, visualization, and governance in a single experience.

AWS AI league is a program that helps organizations upskill their workforce by combining fun competition with hands-on learning using AWS AI services. It offers a unique opportunity for both enterprises and developers to gain valuable and practical skills in fine-tuning, model customization, and prompt engineering - essential skills for building generative AI solutions.

Amazon SageMaker Unified Studio is a single data and AI development platform that brings data together with analytics and AI/ML tools, including Amazon Bedrock and Amazon Q Developer, to streamline analytics and AI application development across virtually any use case.

Amazon S3 Tables integration with SageMaker Lakehouse enables unified access to S3 Tables data from AWS analytics engines like Amazon Athena, Redshift, EMR, and third-party query engines, to build securely and manage centrally.

Amazon SageMaker Unified Studio is a single data and AI development platform that brings data together with analytics and AI/ML tools, including Amazon Bedrock and Amazon Q Developer, to streamline analytics and AI application development across virtually any use case.

Amazon S3 Tables integration with SageMaker Lakehouse enables unified access to S3 Tables data from AWS analytics engines like Amazon Athena, Redshift, EMR, and third-party query engines, to build securely and manage centrally.