Today, we announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services...
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 becomes unwieldy as organizations scale...
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, tools, and workflows of Amazon...
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 development tools, custom extensions, and...
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, and deploy models on the...
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 generative AI development.
As customers across...
