AWS Machine Learning Blog

Category: Advanced (300)

Amazon Bedrock Prompt Optimization Drives LLM Applications Innovation for Yuewen Group

Today, we are excited to announce the availability of Prompt Optimization on Amazon Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the Amazon Bedrock console. In this blog post, we discuss how Prompt Optimization improves the performance of large language models (LLMs) for intelligent text processing task in Yuewen Group.

Build an automated generative AI solution evaluation pipeline with Amazon Nova

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.

Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation model

Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation model

In this post, we use the multi-agent feature of Amazon Bedrock to demonstrate a powerful and innovative approach to AWS cost management. By using the advanced capabilities of Amazon 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.

full view of the Supervisor Agent with its sub-agents

Build multi-agent systems with LangGraph and Amazon Bedrock

This post demonstrates how to integrate open-source multi-agent framework, LangGraph, with Amazon Bedrock. It explains how to use LangGraph and Amazon Bedrock to build powerful, interactive multi-agent applications that use graph-based orchestration.

Model customization, RAG, or both: A case study with Amazon Nova

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.

Boost team productivity with Amazon Q Business Insights

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.

Multi-LLM routing strategies for generative AI applications on AWS

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements. The multi-LLM approach enables organizations to effectively choose the right model for each task, adapt to different […]

How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference

In this post, we show how iFood uses SageMaker to revolutionize its ML operations. By harnessing the power of SageMaker, iFood streamlines the entire ML lifecycle, from model training to deployment. This integration not only simplifies complex processes but also automates critical tasks.

Build an enterprise synthetic data strategy using Amazon Bedrock

In this post, we explore how to use Amazon Bedrock for synthetic data generation, considering these challenges alongside the potential benefits to develop effective strategies for various applications across multiple industries, including AI and machine learning (ML).

Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI

Ray is an open source framework that makes it straightforward to create, deploy, and optimize distributed Python jobs. In this post, we demonstrate the steps involved in running Ray jobs on SageMaker HyperPod.