Introducing one-click onboarding of existing datasets to Amazon SageMaker
Amazon SageMaker introduces one-click onboarding of existing AWS datasets to Amazon SageMaker Unified Studio. This helps AWS customers to start working with their data in minutes, using their existing AWS Identity and Access Management (IAM) roles and permissions. Customers can start working with any data they have access to using a new serverless notebook with a built-in AI agent. This new notebook, which supports SQL, Python, Spark or natural language, gives data engineers, analysts, and data scientists a single high-performance interface to develop and run both SQL queries and code. Customers also have access to many other existing tools such as a Query Editor for SQL analysis, JupyterLab IDE, Visual ETL and workflows, and machine learning (ML) capabilities. The ML capabilities include the ability to discover foundation models from a centralized model hub, customize them with sample notebooks, use MLflow for experimentation, publish trained models in the model hub for discovery, and deploy them as inference endpoints for prediction.
Customers can start directly from Amazon SageMaker, Amazon Athena, Amazon Redshift, and Amazon S3 Tables console pages, giving them a fast path from their existing tools and data to the simple experience in SageMaker Unified Studio. After clicking ‘Get started’ and specifying an IAM role, SageMaker prompts for specific policy updates and then automatically creates a project in SageMaker Unified Studio. The project is set up with all existing data permissions from AWS Glue Data Catalog, AWS Lake Formation, and Amazon S3, and a notebook and serverless compute are pre-configured to accelerate first use.
To get started, simply click "Get Started" from the SageMaker console or open SageMaker Unified Studio from Amazon Athena, Amazon Redshift, or Amazon S3 Tables. One-click onboarding of existing datasets is available in all Regions where Amazon SageMaker Unified Studio is supported. To learn more read the AWS News Blog or visit the Amazon SageMaker documentation.