AWS Machine Learning Blog

In this post, we demonstrate how you can use custom plugins for Amazon Q Business to build a chatbot that can interact with multiple APIs using natural language prompts. We showcase how to build an AIOps chatbot that enables users to interact with their AWS infrastructure through natural language queries and commands. The chatbot is capable of handling tasks such as querying the data about Amazon Elastic Compute Cloud (Amazon EC2) ports and Amazon Simple Storage Service (Amazon S3) buckets access settings.

This post describes how the AWS Customer Channel Technology – Localization Team worked with TransPerfect to integrate Amazon Bedrock into the GlobalLink translation management system, a cloud-based solution designed to help organizations manage their multilingual content and translation workflows. Organizations use TransPerfect’s solution to rapidly create and deploy content at scale in multiple languages using AI.

The AWS LLM League was designed to lower the barriers to entry in generative AI model customization by providing an experience where participants, regardless of their prior data science experience, could engage in fine-tuning LLMs. Using Amazon SageMaker JumpStart, attendees were guided through the process of customizing LLMs to address real business challenges adaptable to their domain.

In this post, we explore the challenges of large-scale frontier model training, focusing on hardware failures and the benefits of Amazon SageMaker HyperPod – a solution that minimizes disruptions, enhances efficiency, and reduces training costs.

The introduction of Amazon Nova models represent a significant advancement in the field of AI, offering new opportunities for large language model (LLM) optimization. In this post, we demonstrate how to effectively perform model customization and RAG with Amazon Nova models as a baseline. We conducted a comprehensive comparison study between model customization and RAG using the latest Amazon Nova models, and share these valuable insights.

In this post, we demonstrate how to use Amazon Personalize and Amazon Bedrock to generate personalized outreach emails for individual users using a video-on-demand use case. This concept can be applied to other domains, such as compelling customer experiences for ecommerce and digital marketing use cases.

In this post, we explore how AI agents can streamline compliance and fulfill regulatory requirements for financial institutions using Amazon Bedrock and CrewAI. We demonstrate how to build a multi-agent system that can automatically summarize new regulations, assess their impact on operations, and provide prescriptive technical guidance. You’ll learn how to use Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents with CrewAI to create a comprehensive, automated compliance solution.

In this post, we demonstrate how to get started with the Pixtral Large model in Amazon Bedrock. The Pixtral Large multimodal model allows you to tackle a variety of use cases, such as document understanding, logical reasoning, handwriting recognition, image comparison, entity extraction, extracting structured data from scanned images, and caption generation.

In this post, we focus specifically on enabling end-users to approve actions and provide feedback using built-in Amazon Bedrock Agents features, specifically HITL patterns for providing safe and effective agent operations. We explore the patterns available using a Human Resources (HR) agent example that helps employees requesting time off.

In this post, we explore Amazon Q Business Insights capabilities and its importance for organizations. We begin with an overview of the available metrics and how they can be used for measuring user engagement and system effectiveness. Then we provide instructions for accessing and navigating this dashboard.