AWS News Blog
Category: Amazon SageMaker Studio
AWS Weekly Roundup: AWS AI/ML Scholars program, Agent Plugin for AWS Serverless, and more (March 30, 2026)
Last week, what excited me most was the launch of the 2026 AWS AI & ML Scholars program by Swami Sivasubramanian, VP of AWS Agentic AI, to provide free AI education to up to 100,000 learners worldwide. The program has two phases: a Challenge phase where you’ll learn foundational generative AI skills, followed by a […]
Introducing Llama 3.2 models from Meta in Amazon Bedrock: A new generation of multimodal vision and lightweight models
Pushing the boundaries of generative AI, Meta unveils Llama 3.2, a groundbreaking language model family featuring enhanced capabilities, broader applicability, and multimodal image support, now available in Amazon Bedrock.
Introducing Amazon Q Developer in SageMaker Studio to streamline ML workflows
Streamline your ML workflows with this generative AI assistant providing tailored guidance, code generation, and error troubleshooting, to build, train, and deploy models efficiently.
AWS Weekly Roundup: Anthropic’s Claude 3 Opus in Amazon Bedrock, Meta Llama 3 in Amazon SageMaker JumpStart, and more (April 22, 2024)
AWS Summits continue to rock the world, with events taking place in various locations around the globe. AWS Summit London (April 24) is the last one in April, and there are nine more in May, including AWS Summit Berlin (May 15–16), AWS Summit Los Angeles (May 22), and AWS Summit Dubai (May 29). Join us […]
Amazon SageMaker Studio adds web-based interface, Code Editor, flexible workspaces, and streamlines user onboarding
Today, we are announcing an improved Amazon SageMaker Studio experience! The new SageMaker Studio web-based interface loads faster and provides consistent access to your preferred integrated development environment (IDE) and SageMaker resources and tooling, irrespective of your IDE choice. In addition to JupyterLab and RStudio, SageMaker Studio now includes a fully managed Code Editor based […]
Package and deploy models faster with new tools and guided workflows in Amazon SageMaker
I’m happy to share that Amazon SageMaker now comes with an improved model deployment experience to help you deploy traditional machine learning (ML) models and foundation models (FMs) faster. As a data scientist or ML practitioner, you can now use the new ModelBuilder class in the SageMaker Python SDK to package models, perform local inference […]