AWS Machine Learning Blog

In this post, we explore how to build a scalable and efficient Retrieval Augmented Generation (RAG) system using the new EMR Serverless integration, Spark’s distributed processing, and an HAQM OpenSearch Service vector database powered by the LangChain orchestration framework. This solution enables you to process massive volumes of textual data, generate relevant embeddings, and store them in a powerful vector database for seamless retrieval and generation.

In this post, we explore a solution that uses the vector engine ChromaDB and Meta Llama 3, a publicly available foundation model hosted on SageMaker JumpStart, for a Text-to-SQL use case. We shared a brief history of Meta Llama 3, best practices for prompt engineering with Meta Llama 3 models, and an architecture pattern using few-shot prompting and RAG to extract the relevant schemas stored as vectors in ChromaDB.

In this post, we provide insights and practical examples to help balance and optimize the prompt engineering workflow. We focus on advanced prompt techniques and best practices for the models provided in HAQM Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies such as Anthropic, Cohere, Meta, Mistral AI, Stability AI, and HAQM through a single API. With these prompting techniques, developers and researchers can harness the full capabilities of HAQM Bedrock, providing clear and concise communication while mitigating potential risks or undesirable outputs.

In this post, we provide a walkthrough of how customers can use generative artificial intelligence (AI) models and LLMs using NVIDIA NIM integration with SageMaker. We demonstrate how this integration works and how you can deploy these state-of-the-art models on SageMaker, optimizing their performance and cost.

The AWS DeepRacer League is the world’s first autonomous racing league, open to everyone and powered by machine learning (ML). AWS DeepRacer brings builders together from around the world, creating a community where you learn ML hands-on through friendly autonomous racing competitions. As we celebrate the achievements of over 560,000 participants from more than 150 countries who sharpened their skills through the AWS DeepRacer League over the last 6 years, we also prepare to close this chapter with a final season that serves as both a victory lap and a launching point for what’s next in the world of AWS DeepRacer.

In this post, we show how you can recommend breaking news to a user using AWS AI/ML services. By taking advantage of the power of HAQM Personalize and HAQM Titan Text Embeddings on HAQM Bedrock, you can show articles to interested users within seconds of them being published.

In this blog post, we will show you how to implement tenant isolation using HAQM Bedrock agents within a multi-tenant environment. We’ll demonstrate this using a sample multi-tenant e-commerce application that provides a service for various tenants to create online stores. This application will use HAQM Bedrock agents to develop an AI assistant or chatbot capable of providing tenant-specific information, such as return policies and user-specific information like order counts and status updates.

In this post, we show you how to perform natural language queries over the indexed GitHub (Cloud) data using the AI-powered chat interface provided by HAQM Q Business. We also cover how HAQM Q Business applies access control lists (ACLs) associated with the indexed documents to provide permissions-filtered responses.

In this post, we show you how to use HAQM Bedrock to automate email responses to customer queries. With our solution, you can identify the intent of customer emails and send an automated response if the intent matches your existing knowledge base or data sources. If the intent doesn’t have a match, the email goes to the support team for a manual response.

In this post, we demonstrate how to seamlessly automate the deployment of an end-to-end RAG solution using Knowledge Bases for HAQM Bedrock and the AWS Cloud Development Kit (AWS CDK), enabling organizations to quickly set up a powerful question answering system.