Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Although Amazon SageMaker Studio provides user-level execution roles, this approach...
As AI models become increasingly sophisticated and specialized, the ability to quickly train and customize models can mean the difference between industry leadership and falling behind. That is why hundreds of thousands of customers use the fully managed infrastructure,...
AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted...
Today, we’re excited to announce that Amazon SageMaker HyperPod now supports deploying foundation models (FMs) from Amazon SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or Amazon FSx. With this launch, you can train, fine-tune,...
Amazon SageMaker now offers fully managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for...
Amazon SageMaker HyperPod now provides a comprehensive, out-of-the-box dashboard that delivers insights into foundation model (FM) development tasks and cluster resources. This unified observability solution automatically publishes key metrics to Amazon Managed Service for Prometheus and visualizes them in...
