AWS Machine Learning Blog
Empowering everyone with GenAI to rapidly build, customize, and deploy apps securely: Highlights from the AWS New York Summit
See how AWS is democratizing generative AI with innovations like Amazon Q Apps to make AI apps from prompts, Amazon Bedrock upgrades to leverage more data sources, new techniques to curtail hallucinations, and AI skills training.
Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs
In this post, we show how to create a multimodal chat assistant on Amazon Web Services (AWS) using Amazon Bedrock models, where users can submit images and questions, and text responses will be sourced from a closed set of proprietary documents.
Design secure generative AI application workflows with Amazon Verified Permissions and Amazon Bedrock Agents
In this post, we demonstrate how to design fine-grained access controls using Verified Permissions for a generative AI application that uses Amazon Bedrock Agents to answer questions about insurance claims that exist in a claims review system using textual prompts as inputs and outputs.
Boost productivity by using AI in cloud operational health management
In this post, we show you how to create an AI-powered, event-driven operations assistant that automatically responds to operational events. The assistant can filter out irrelevant events (based on your organization’s policies), recommend actions, create and manage issue tickets in integrated IT service management (ITSM) tools to track actions, and query knowledge bases for insights related to operational events.
How Indeed builds and deploys fine-tuned LLMs on Amazon SageMaker
In this post, we describe how using the capabilities of Amazon SageMaker has accelerated Indeed’s AI research, development velocity, flexibility, and overall value in our pursuit of using Indeed’s unique and vast data to leverage advanced LLMs.
Improve LLM application robustness with Amazon Bedrock Guardrails and Amazon Bedrock Agents
In this post, we demonstrate how Amazon Bedrock Guardrails can improve the robustness of the agent framework. We are able to stop our chatbot from responding to non-relevant queries and protect personal information from our customers, ultimately improving the robustness of our agentic implementation with Amazon Bedrock Agents.
Dive deep into vector data stores using Amazon Bedrock Knowledge Bases
In this post, we dive deep into the vector database options available as part of Amazon Bedrock Knowledge Bases and the applicable use cases, and look at working code examples.
Enable or disable ACL crawling safely in Amazon Q Business
Amazon Q Business recently added support for administrators to modify the default access control list (ACL) crawling feature for data source connectors. Amazon Q Business is a fully managed, AI powered assistant with enterprise-grade security and privacy features. It includes over 40 data source connectors that crawl and index documents. By default, Amazon Q Business […]
SK Telecom improves telco-specific Q&A by fine-tuning Anthropic’s Claude models in Amazon Bedrock
In this post, we share how SKT customizes Anthropic Claude models for telco-specific Q&A regarding technical telecommunication documents of SKT using Amazon Bedrock.
Scaling Rufus, the Amazon generative AI-powered conversational shopping assistant with over 80,000 AWS Inferentia and AWS Trainium chips, for Prime Day
In this post, we dive into the Rufus inference deployment using AWS chips and how this enabled one of the most demanding events of the year—Amazon Prime Day.
Exploring alternatives and seamlessly migrating data from Amazon Lookout for Vision
In this post we discuss how you can maintain access to Lookout for Vision after it is closed to new customers, some alternatives to Lookout for Vision, and how you can export your data from Lookout for Vision to migrate to an alternate solution.