AWS Machine Learning Blog

See how AWS is democratizing generative AI with innovations like Amazon Q Apps to make AI apps from prompts, Amazon Bedrock upgrades to leverage more data sources, new techniques to curtail hallucinations, and AI skills training.

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM), making it easier to securely share and discover machine learning (ML) models across your AWS accounts. In this post, we will show you how to use this new cross-account model sharing feature to build your own centralized model governance capability, which is often needed for centralized model approval, deployment, auditing, and monitoring workflows.

In this post, we show you how to build a generative AI-powered trip-planning service that revolutionizes the way travelers discover and explore destinations. By using advanced AI technology and Amazon Location Service, the trip planner lets users translate inspiration into personalized travel itineraries. This innovative service goes beyond traditional trip planning methods, offering real-time interaction through a chat-based interface and maintaining scalability, reliability, and data security through AWS native services.

This post explores a solution that uses the power of AWS generative AI capabilities like Amazon Bedrock and OpenSearch vector search to perform damage appraisals for insurers, repair shops, and fleet managers.

Stability AI’s newest launch of Stable Diffusion 3.5 Large (SD3.5L) on Amazon SageMaker JumpStart enhances image generation, human anatomy rendering, and typography by producing more diverse outputs and adhering closely to user prompts, making it a significant upgrade over its predecessor. In this post, we explore advanced prompt engineering techniques that can enhance the performance of these models and facilitate the creation of compelling imagery through text-to-image transformations.

We are excited to announce the availability of Stability AI’s latest and most advanced text-to-image model, Stable Diffusion 3.5 Large, in Amazon SageMaker JumpStart. In this post, we provide an implementation guide for subscribing to Stable Diffusion 3.5 Large in SageMaker JumpStart, deploying the model in Amazon SageMaker Studio, and generating images using text-to-image prompts.

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards, making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks. In this post, we discuss a new feature that supports the integration of model cards with the model registry. We discuss the solution architecture and best practices for managing model cards with a registered model version, and walk through how to set up, operationalize, and govern your models using the integration in the model registry.

In this post, we explore the approach behind building an AWS AI-powered Chrome extension that aims to revolutionize the live streaming experience by providing real-time transcription, translation, and summarization capabilities directly within your browser.

In this post, we demonstrate how to deploy a generative AI application that can accelerate your financial statement analysis on AWS.

This post outlines a custom multilingual document extraction and content assessment framework using a combination of Anthropic’s Claude 3 on Amazon Bedrock and Amazon A2I to incorporate human-in-the-loop capabilities.

In this post, you will learn how to extract key objects from image queries using Amazon Rekognition and build a reverse image search engine using Amazon Titan Multimodal Embeddings from Amazon Bedrock in combination with Amazon OpenSearch Serverless Service.