Intermediate (200)

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 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.

Amazon Bedrock Knowledge Bases has a metadata filtering capability that allows you to refine search results based on specific attributes of the documents, improving retrieval accuracy and the relevance of responses. These metadata filters can be used in combination with the typical semantic (or hybrid) similarity search. In this post, we discuss using metadata filters with Amazon Bedrock Knowledge Bases.

This post explains how to integrate Smartsheet with Amazon Q Business to use natural language and generative AI capabilities for enhanced insights. Smartsheet, the AI-enhanced enterprise-grade work management platform, helps users manage projects, programs, and processes at scale.

In this post, we show how Anthropic’s Claude 3.5 Sonnet in Amazon Bedrock can be used for a variety of business-related cognitive tasks, such as problem-solving, critical thinking and ideation—to help augment human thinking and improve decision-making among knowledge workers to accelerate innovation.

In this post, we demonstrate how to implement this evaluation framework using Amazon Bedrock, compare the performance of different generator models, including Anthropic’s Claude and Amazon Nova on Amazon Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.

Starter kits are complete, deployable solutions that address common, repeatable business problems. They deploy the services that make up a solution according to best practices, helping you optimize costs and become familiar with these kinds of architectural patterns without a large investment in training. In this post, we showcase a starter kit for Amazon Q Business. If you have a repository of documents that you need to turn into a knowledge base quickly, or simply want to test out the capabilities of Amazon Q Business without a large investment of time at the console, then this solution is for you.

The solution presented in this post demonstrates a powerful pattern for accelerating video and audio review workflows while maintaining human oversight. By combining the power of AI models in Amazon Bedrock with human expertise, you can create tools that not only boost productivity but also maintain the critical element of human judgment in important decision-making processes.

In this post, we show how you can use our enterprise graph machine learning (GML) framework GraphStorm to solve prediction challenges on large-scale complex networks inspired by our practices of exploring GML to mitigate the AWS backbone network congestion risk.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. In this post, I’ll show you how to use Amazon Bedrock—with its fully managed, on-demand API—with your Amazon SageMaker trained or fine-tuned model.

In this post, we walk you through the process to build an automated mechanism using Amazon SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the Amazon SageMaker Model Registry for your customers to use it.