Category: Artificial Intelligence

In this post, we walk through how to build the FoodSavr solution (fictitious name used for the purposes of this post) using Amazon Rekognition Custom Labels to detect the ingredients and generate personalized recipes using Anthropic’s Claude 3.0 on Amazon Bedrock. We demonstrate an end-to-end architecture where a user can upload an image of their fridge, and using the ingredients found there (detected by Amazon Rekognition), the solution will give them a list of recipes (generated by Amazon Bedrock). The architecture also recognizes missing ingredients and provides the user with a list of nearby grocery stores.

In this post, we demonstrate how Octus migrated its flagship product, CreditAI, to Amazon Bedrock, transforming how investment professionals access and analyze credit intelligence. We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate, and Amazon OpenSearch Service.

In this post, we demonstrate how to optimize reasoning models like DeepSeek-R1 using prompt optimization on Amazon Bedrock.

Today, we’re announcing the general availability (GA) of multi-agent collaboration on Amazon Bedrock. This capability allows developers to build, deploy, and manage networks of AI agents that work together to execute complex, multi-step workflows efficiently.

This post is co-authored with Sundeep Sardana, Malolan Raman, Joseph Lam, Maitri Shah and Vaibhav Singh from Verisk. Verisk (Nasdaq: VRSK) is a leading strategic data analytics and technology partner to the global insurance industry, empowering clients to strengthen operating efficiency, improve underwriting and claims outcomes, combat fraud, and make informed decisions about global risks. […]

Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.

In this post, we explore how to use LangGraph and Mistral models on Amazon Bedrock to create a powerful multi-agent system that can handle sophisticated workflows through collaborative problem-solving. This integration enables the creation of AI agents that can work together to solve complex problems, mimicking humanlike reasoning and collaboration.

In this post, we’ll explore how to leverage Amazon Bedrock, LlamaIndex, and RAGAS to enhance your RAG implementations. You’ll learn practical techniques to evaluate and optimize your AI systems, enabling more accurate, context-aware responses that align with your organization’s specific needs.

In this post, we explain how BMW uses generative AI to speed up the root cause analysis of incidents in complex and distributed systems in the cloud such as BMW’s Connected Vehicle backend serving 23 million vehicles. Read on to learn how the solution, collaboratively pioneered by AWS and BMW, uses Amazon Bedrock Agents and Amazon CloudWatch logs and metrics to find root causes quicker. This post is intended for cloud solution architects and developers interested in speeding up their incident workflows.

In this blog post, we will guide you through the process of integrating Chronos into Amazon SageMaker Pipeline using a synthetic dataset that simulates a sales forecasting scenario, unlocking accurate and efficient predictions with minimal data.