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This post is co-written with Vlad Lebedev and DJ Charles from Mixbook. Mixbook is an award-winning design platform that gives users unrivaled creative freedom to design and share one-of-a-kind stories, transforming the lives of more than six million people. Today, Mixbook is the #1 rated photo book service in the US with 26 thousand five-star …

How Mixbook used generative AI to offer personalized photo book experiences Read More »

In the diverse toolkit available for deploying cloud infrastructure, Agents for Amazon Bedrock offers a practical and innovative option for teams looking to enhance their infrastructure as code (IaC) processes. Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks. After being configured, an agent builds the prompt and augments it with …

Using Agents for Amazon Bedrock to interactively generate infrastructure as code Read More »

Retrieval Augmented Generation (RAG) is a popular paradigm that provides additional knowledge to large language models (LLMs) from an external source of data that wasn’t present in their training corpus. RAG provides additional knowledge to the LLM through its input prompt space and its architecture typically consists of the following components: Indexing: Prepare a corpus …

Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker Read More »

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 …

How BRIA AI used distributed training in Amazon SageMaker to train latent diffusion foundation models for commercial use Read More »

Amazon SageMaker Studio provides a comprehensive suite of fully managed integrated development environments (IDEs) for machine learning (ML), including JupyterLab, Code Editor (based on Code-OSS), and RStudio. It supports all stages of ML development—from data preparation to deployment, and allows you to launch a preconfigured JupyterLab IDE for efficient coding within seconds. Additionally, its flexible …

Create custom images for geospatial analysis with Amazon SageMaker Distribution in Amazon SageMaker Studio Read More »

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) …

Automating model customization in Amazon Bedrock with AWS Step Functions workflow Read More »

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 …

Knowledge Bases for Amazon Bedrock now supports advanced parsing, chunking, and query reformulation giving greater control of accuracy in RAG based applications Read More »

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 …

Streamline generative AI development in Amazon Bedrock with Prompt Management and Prompt Flows (preview) Read More »

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 …

Empowering everyone with GenAI to rapidly build, customize, and deploy apps securely: Highlights from the AWS New York Summit Read More »

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 …

A progress update on our commitment to safe, responsible generative AI Read More »