New capabilities in Amazon SageMaker AI continue to transform how organizations develop AI models

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 SageMaker AI to scale and advance AI model development. Since launching … Read more

On Grok and the Weight of Design

There’s a difference between drift and direction. Between a model veering off course, and one gently nudged there. Recent findings—such as those outlined in Emergent Misalignment (arXiv:2502.17424)—demonstrate how targeted fine-tuning, even when applied narrowly, can ripple outward through a model’s broader behavior. Adjustments intended to steer responses in one domain can unintentionally distort outputs in … Read more

Accelerate foundation model development with one-click observability in Amazon SageMaker HyperPod

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 Amazon Managed Grafana dashboards, optimized specifically for FM development with deep coverage of hardware health, resource … Read more

Accelerating generative AI development with fully managed MLflow 3.0 on Amazon SageMaker AI

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 industries accelerate their generative AI development, they require capabilities to … Read more

Amazon SageMaker HyperPod launches model deployments to accelerate the generative AI model development lifecycle

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 same HyperPod compute resources, maximizing resource utilization across the entire model … Read more

Supercharge your AI workflows by connecting to SageMaker Studio from Visual Studio Code

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 debugging tools while accessing compute resources and your data in SageMaker … Read more