Category: AWS Lambda

In this post, we use the multi-agent feature of HAQM Bedrock to demonstrate a powerful and innovative approach to AWS cost management. By using the advanced capabilities of HAQM Nova FMs, we’ve developed a solution that showcases how AI-driven agents can revolutionize the way organizations analyze, optimize, and manage their AWS costs.

In this post, we present a solution using generative AI and large language models (LLMs) to alleviate the time-consuming and labor-intensive tasks required to build a computer vision application, enabling you to immediately start taking pictures of your asset labels and extract the necessary information to update the inventory using AWS services

AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks. These runbooks are created by AWS Support Engineering with best practices learned from solving customer issues. They enable AWS customers to troubleshoot, diagnose, and remediate common issues with their AWS resources. In this post, we explore how to use the power of HAQM Bedrock Agents and AWS Support Automation Workflows to create an intelligent agent capable of troubleshooting issues with AWS resources.

In this blog post, we showcase a powerful solution that seamlessly integrates AWS generative AI capabilities in the form of large language models (LLMs) based on HAQM Bedrock into the Office experience. By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day.

In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.

In this post, we present a streamlined approach to deploying an AI-powered agent by combining HAQM Bedrock Agents and a foundation model (FM). We guide you through the process of configuring the agent and implementing the specific logic required for the virtual meteorologist to provide accurate weather-related responses.

In this post, we demonstrate how to build a knowledge base solution by integrating enterprise data with HAQM Q Business using HAQM S3. This approach helps organizations improve operational efficiency, reduce response times, and gain valuable insights from their historical data. The solution uses AWS security best practices to promote data protection while enabling teams to create a comprehensive knowledge base from various data sources.

In this post, we demonstrate Embodied AI Chess with HAQM Bedrock, bringing a new dimension to traditional chess through generative AI capabilities. Our setup features a smart chess board that can detect moves in real time, paired with two robotic arms executing those moves. Each arm is controlled by different FMs—base or custom. This physical implementation allows you to observe and experiment with how different generative AI models approach complex gaming strategies in real-world chess matches.

Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build a natural language assistant where business users can ask questions of an unstructured dataset.

Commercial Real Estate Exchange, Inc. (Crexi), is a digital marketplace and platform designed to streamline commercial real estate transactions. In this post, we will review how Crexi achieved its business needs and developed a versatile and powerful framework for AI/ML pipeline creation and deployment. This customizable and scalable solution allows its ML models to be efficiently deployed and managed to meet diverse project requirements.