Category: Artificial Intelligence

In this post, we explore the importance of evaluating LLMs in the context of generative AI applications, highlighting the challenges posed by issues like hallucinations and biases. We introduced a comprehensive solution using AWS services to automate the evaluation process, allowing for continuous monitoring and assessment of LLM performance. By using tools like the FMeval Library, Ragas, LLMeter, and Step Functions, the solution provides flexibility and scalability, meeting the evolving needs of LLM consumers.

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.

For this post, we implement a RAG architecture with HAQM Bedrock Knowledge Bases using a custom connector and topics built with HAQM Managed Streaming for Apache Kafka (HAQM MSK) for a user who may be interested to understand stock price trends.

This post demonstrates how Zoom users can access their HAQM Q Business enterprise data directly within their Zoom interface, alleviating the need to switch between applications while maintaining enterprise security boundaries. Organizations can now configure Zoom as a data accessor in HAQM Q Business, enabling seamless integration between their HAQM Q index and Zoom AI Companion. This integration allows users to access their enterprise knowledge in a controlled manner directly within the Zoom platform.

In this post, we explore how QyrusAI and HAQM Bedrock are revolutionizing shift-left testing, enabling teams to deliver better software faster. HAQM Bedrock is a fully managed service that allows businesses to build and scale generative AI applications using foundation models (FMs) from leading AI providers. It enables seamless integration with AWS services, offering customization, security, and scalability without managing infrastructure.

HAQM Bedrock Data Automation (BDA) is a new managed feature powered by FMs in HAQM Bedrock. BDA extracts structured outputs from unstructured content—including documents, images, video, and audio—while alleviating the need for complex custom workflows. In this post, we demonstrate how BDA automatically extracts rich video insights such as chapter segments and audio segments, detects text in scenes, and classifies Interactive Advertising Bureau (IAB) taxonomies, and then uses these insights to build a nonlinear ads solution to enhance contextual advertising effectiveness.

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Model Serving team is working to push the boundaries of natural language processing and AI capabilities for enterprise applications. Their key focus areas include optimizing large […]

In this post, we demonstrate how to orchestrate multiple HAQM Bedrock agents to create a sophisticated HAQM EKS troubleshooting system. By enabling collaboration between specialized agents—deriving insights from K8sGPT and performing actions through the ArgoCD framework—you can build a comprehensive automation that identifies, analyzes, and resolves cluster issues with minimal human intervention.

In this post, we explore how Low-Rank Adaptation (LoRA) can be used to address these challenges effectively. Specifically, we discuss using LoRA serving with LoRA eXchange (LoRAX) and HAQM Elastic Compute Cloud (HAQM EC2) GPU instances, allowing organizations to efficiently manage and serve their growing portfolio of fine-tuned models, optimize costs, and provide seamless performance for their customers.

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