Category: Amazon Machine Learning

In this post, we explore how to build a conversational device management system using Amazon Bedrock AgentCore. With this solution, users can manage their IoT devices through natural language, using a UI for tasks like checking device status, configuring WiFi networks, and monitoring user activity.

This post shows how Salesforce integrated Amazon Bedrock Custom Model Import into their machine learning operations (MLOps) workflow, reused existing endpoints without application changes, and benchmarked scalability. We share key metrics on operational efficiency and cost optimization gains, and offer practical insights for simplifying your deployment strategy.

In this post, we demonstrate the development of a conceptual Medical Reports Analysis Dashboard that combines Amazon Bedrock AI capabilities, LangChain’s document processing, and Streamlit’s interactive visualization features. The solution transforms complex medical data into accessible insights through a context-aware chat system powered by large language models available through Amazon Bedrock and dynamic visualizations of health parameters.

In this post, we explore how Amazon Quick Suite’s Model Context Protocol (MCP) client enables secure, standardized connections to enterprise applications and AI agents, eliminating the need for complex custom integrations. You’ll discover how to set up MCP Actions integrations with popular enterprise tools like Atlassian Jira and Confluence, AWS Knowledge MCP Server, and Amazon Bedrock AgentCore Gateway to create a collaborative environment where people and AI agents can seamlessly work together across your organization’s data and applications.

Learn why customers choose AgentCore to build secure, reliable AI solutions using their choice of frameworks and models for production workloads.

In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for distributed AI workloads with high-performance hardware, continuous monitoring, and seamless integration with Ray, the leading AI compute engine, enabling organizations to reduce time-to-market and lower total cost of ownership.

In this post, we introduce Amazon Nova customization for text content moderation through Amazon SageMaker AI, enabling organizations to fine-tune models for their specific moderation needs. The evaluation across three benchmarks shows that customized Nova models achieve an average improvement of 7.3% in F1 scores compared to the baseline Nova Lite, with individual improvements ranging from 4.2% to 9.2% across different content moderation tasks.

In this post, we show how Vxceed used Amazon Bedrock to develop this AI-powered multi-agent solution that generates personalized sales pitches for field sales teams at scale.

In this post, we demonstrate how Amazon Nova Act automates QuickSight data story creation, saving time so you can focus on making critical, data-driven business decisions.

Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline operations, and drive innovation. As generative AI workloads continue to grow in scale and importance, organizations face new challenges in maintaining consistent performance, reliability, and availability of their AI-powered applications. Customers are looking to scale their AI inference workloads across […]