Category: Amazon SageMaker

Amazon Bedrock now offers Stability AI Image Services: 9 tools that improve how businesses create and modify images. The technology extends Stable Diffusion and Stable Image models to give you precise control over image creation and editing. Clear prompts are critical—they provide art direction to the AI system. Strong prompts control specific elements like tone, […]

In this post, we show how to integrate AWS DLCs with MLflow to create a solution that balances infrastructure control with robust ML governance. We walk through a functional setup that your team can use to meet your specialized requirements while significantly reducing the time and resources needed for ML lifecycle management.

In this post, we show how to deploy gpt-oss-20b model to SageMaker managed endpoints and demonstrate a practical stock analyzer agent assistant example with LangGraph, a powerful graph-based framework that handles state management, coordinated workflows, and persistent memory systems.

In this post, we introduce topology-aware scheduling with SageMaker HyperPod task governance by submitting jobs that represent hierarchical network information. We provide details about how to use SageMaker HyperPod task governance to optimize your job efficiency.

In this post, we walk through how to streamline your RAG development lifecycle from experimentation to automation, helping you operationalize your RAG solution for production deployments with Amazon SageMaker AI, helping your team experiment efficiently, collaborate effectively, and drive continuous improvement.

In this post, we present a complete solution for programmatically creating private workforces on Amazon SageMaker AI using the AWS Cloud Development Kit (AWS CDK), including the setup of a dedicated, fully configured Amazon Cognito user pool.

As generative AI continues to transform how enterprises operate—and develop net new innovations—the infrastructure demands for training and deploying AI models have grown exponentially. Traditional infrastructure approaches are struggling to keep pace with today’s computational requirements, network demands, and resilience needs of modern AI workloads. At AWS, we’re also seeing a transformation across the technology […]

AWS announced managed tiered checkpointing in Amazon SageMaker HyperPod, a purpose-built infrastructure to scale and accelerate generative AI model development across thousands of AI accelerators. Managed tiered checkpointing uses CPU memory for high-performance checkpoint storage with automatic data replication across adjacent compute nodes for enhanced reliability. In this post, we dive deep into those concepts and understand how to use the managed tiered checkpointing feature.

We are excited to announce the general availability of fine-grained compute and memory quota allocation with HyperPod task governance. With this capability, customers can optimize Amazon SageMaker HyperPod cluster utilization on Amazon Elastic Kubernetes Service (Amazon EKS), distribute fair usage, and support efficient resource allocation across different teams or projects. For more information, see HyperPod task governance best […]

In this post, we demonstrate how a research university implemented SageMaker HyperPod to accelerate AI research by using dynamic SLURM partitions, fine-grained GPU resource management, budget-aware compute cost tracking, and multi-login node load balancing—all integrated seamlessly into the SageMaker HyperPod environment.