Tag: Amazon SageMaker

This post was written with Alex Gnibus of Stability AI. Stability AI Image Services are now available in Amazon Bedrock, offering ready-to-use media editing capabilities delivered through the Amazon Bedrock API. These image editing tools expand on the capabilities of Stability AI’s Stable Diffusion 3.5 models (SD3.5) and Stable Image Core and Ultra models, which […]

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.

The art of storyboarding stands as the cornerstone of modern content creation, weaving its essential role through filmmaking, animation, advertising, and UX design. Though traditionally, creators have relied on hand-drawn sequential illustrations to map their narratives, today’s AI foundation models (FMs) are transforming this landscape. FMs like Amazon Nova Canvas and Amazon Nova Reel offer […]

In this post, we walk through how you can use the new Code Editor and multiple spaces support in SageMaker Unified Studio. The sample solution shows how to develop an ML pipeline that automates the typical end-to-end ML activities to build, train, evaluate, and (optionally) deploy an ML model.

In this post, we demonstrate how to enhance AI agents’ capabilities by integrating predictive ML models using Amazon SageMaker AI and the MCP. By using the open source Strands Agents SDK and the flexible deployment options of SageMaker AI, developers can create sophisticated AI applications that combine conversational AI with powerful predictive analytics capabilities.

In this post, we focus on implementing this architecture with step-by-step guidance and reference code. We provide a detailed technical walkthrough that addresses the needs of two critical personas in the AI development lifecycle: the administrator who establishes governance and infrastructure through automated templates, and the data scientist who uses SageMaker Unified Studio for model development without managing the underlying infrastructure.

This post presents architectural strategies and a scalable framework that helps organizations manage multi-tenant environments, automate consistently, and embed governance controls as they scale their AI initiatives with SageMaker Unified Studio.

Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI, […]

In this post, we explore how SageMaker and federated learning help financial institutions build scalable, privacy-first fraud detection systems.

In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using SageMaker AI inference for real-time inference. We also discuss best practices to choose the right instance size and share some debugging best practices while working with JupyterLab notebooks in SageMaker Unified Studio.