AWS Machine Learning Blog

Years ago, Mixbook undertook a strategic initiative to transition their operational workloads to Amazon Web Services (AWS), a move that has continually yielded significant advantages. This pivotal decision has been instrumental in propelling them towards fulfilling their mission, ensuring their system operations are characterized by reliability, superior performance, and operational efficiency. In this post we show you how Mixbook used generative artificial intelligence (AI) capabilities in AWS to personalize their photo book experiences—a step towards their mission.

In this blog post, we explore how Agents for Amazon Bedrock can be used to generate customized, organization standards-compliant IaC scripts directly from uploaded architecture diagrams. This will help accelerate deployments, reduce errors, and ensure adherence to security guidelines.

This post demonstrates how to use Amazon SageMaker to fine tune a Sentence Transformer embedding model and deploy it with an Amazon SageMaker Endpoint. The code from this post and more examples are available in the GitHub repo.

This post is co-written with Bar Fingerman from BRIA AI. This post explains how BRIA AI trained BRIA AI 2.0, a high-resolution (1024×1024) text-to-image diffusion model, on a dataset comprising petabytes of licensed images quickly and economically. Amazon SageMaker training jobs and Amazon SageMaker distributed training libraries took on the undifferentiated heavy lifting associated with infrastructure […]

This post shows you how to extend Amazon SageMaker Distribution with additional dependencies to create a custom container image tailored for geospatial analysis. Although the example in this post focuses on geospatial data science, the methodology presented can be applied to any kind of custom image based on SageMaker Distribution.

Large language models have become indispensable in generating intelligent and nuanced responses across a wide variety of business use cases. However, enterprises often have unique data and use cases that require customizing large language models beyond their out-of-the-box capabilities. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) […]

Knowledge Bases for Amazon Bedrock is a fully managed service that helps you implement the entire Retrieval Augmented Generation (RAG) workflow from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows, pushing the boundaries for what you can do in your RAG workflows. However, it’s […]

Today, we’re excited to introduce two powerful new features for Amazon Bedrock: Prompt Management and Prompt Flows, in public preview. These features are designed to accelerate the development, testing, and deployment of generative artificial intelligence (AI) applications, enabling developers and business users to create more efficient and effective solutions that are easier to maintain. You […]

Imagine this—all employees relying on generative artificial intelligence (AI) to get their work done faster, every task becoming less mundane and more innovative, and every application providing a more useful, personal, and engaging experience. To realize this future, organizations need more than a single, powerful large language model (LLM) or chat assistant. They need a […]

Responsible AI is a longstanding commitment at Amazon. From the outset, we have prioritized responsible AI innovation by embedding safety, fairness, robustness, security, and privacy into our development processes and educating our employees. We strive to make our customers’ lives better while also establishing and implementing the necessary safeguards to help protect them. Our practical […]