Category: *Post Types

We’re excited to announce the open source release of AWS MCP Servers for code assistants — a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers. In this post, we walk through how these specialized MCP servers can dramatically reduce your development time while incorporating security controls, cost optimizations, and AWS Well-Architected best practices into your code.

To improve factual accuracy of large language model (LLM) responses, AWS announced Amazon Bedrock Automated Reasoning checks (in gated preview) at AWS re:Invent 2024. In this post, we discuss how to help prevent generative AI hallucinations using Amazon Bedrock Automated Reasoning checks.

In a recent AWS What’s New Post, App Studio announced two new features to accelerate application building: Prebuilt solutions catalog and cross-instance Import and Export. In this post, we walk through how to use the prebuilt solutions catalog to get started quickly and use the Import and Export feature

Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. In this post, we discuss how to get started with image content filters in Amazon Bedrock Guardrails.

When implementing machine learning workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMaker Canvas. This post provides an example of how to incorporate code that relies on external dependencies into your SageMaker Canvas workflows.

Today, we are announcing an enhanced private hub feature with several new capabilities that give organizations greater control over their ML assets. These enhancements include the ability to fine-tune SageMaker JumpStart models directly within the private hub, support for adding and managing custom-trained models, deep linking capabilities for associated notebooks, and improved model version management.

In this post, we discuss how to evaluate the performance of your knowledge base, including the metrics and data to use for evaluation. We also address some of the tactics and configuration changes that can improve specific metrics.

Discover how to build a GenAI powered virtual IT troubleshooting assistant using Amazon Q Business. This innovative solution integrates with popular ITSM tools like ServiceNow, Atlassian Jira, and Confluence to streamline information retrieval and enhance collaboration across your organization. By harnessing the power of generative AI, this assistant can significantly boost operational efficiency and provide 24/7 support tailored to individual needs. Learn how to set up, configure, and leverage this solution to transform your enterprise information management.

This post presents a comprehensive AIOps solution that combines various AWS services such as Amazon Bedrock, AWS Lambda, and Amazon CloudWatch to create an AI assistant for effective incident management. This solution also uses Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents. The solution uses the power of Amazon Bedrock to enable the deployment of intelligent agents capable of monitoring IT systems, analyzing logs and metrics, and invoking automated remediation processes.

In this post, we demonstrate how to use Amazon Bedrock in SageMaker Unified Studio to build a generative AI application to integrate with an existing endpoint and database.