Category: AWS Identity and Access Management (IAM)

In this post, you’ll learn how fine-tuning Amazon Nova models using Amazon SageMaker AI addresses these specific issues by teaching the models to recognize your exact data patterns, distinguish between similar fields, and process information more efficiently—achieving up to 94.77% extraction accuracy while reducing costs 50%.

This post shows how to build a governed, serverless data mesh on AWS that provides the secure, scalable data foundation production agentic AI requires.

This solution combines the power of Amazon Bedrock AgentCore, Strands Agents, and Amazon Quick transforms to deliver a secure, scalable, and intelligent system for building and operating AI agents while transforming data into actionable business insights.

In this post, we show you how to build a similar system for your organization. You will learn the architecture decisions, implementation details, and deployment process that can help you automate your own compliance workflows.

In this post, we show you how to use Amazon SageMaker AI to build and deploy a deep learning model for detecting solar flares using data from the European Space Agency’s STIX instrument.

Machine learning operations (MLOps) is the combination of people, processes, and technology to productionize ML use cases efficiently. To achieve this, enterprise customers must develop MLOps platforms to support reproducibility, robustness, and end-to-end observability of the ML use case’s lifecycle. Those platforms are based on a multi-account setup by adopting strict security constraints, development best […]

In this post, we demonstrate how to use SageMaker Unified Studio and AWS Identity and Access Management (IAM) to establish a robust permission framework for Amazon Bedrock models. We show how administrators can precisely manage which users and teams have access to specific models within a secure, collaborative environment. We guide you through creating granular permissions to control model access, with code examples for common enterprise governance scenarios.

In this post, we discuss how an enterprise with multiple accounts can access a shared Amazon SageMaker HyperPod cluster for running their heterogenous workloads. We use SageMaker HyperPod task governance to enable this feature.

In this post, we present a solution for securing distributed logging multi-account deployments using Amazon Bedrock and LangChain.

In this post, we demonstrate how to use H-optimus-0 for two common digital pathology tasks: patch-level analysis for detailed tissue examination, and slide-level analysis for broader diagnostic assessment. Through practical examples, we show you how to adapt this FM to these specific use cases while optimizing computational resources.