Category: HAQM Machine Learning

The introduction of HAQM 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 HAQM Nova models as a baseline. We conducted a comprehensive comparison study between model customization and RAG using the latest HAQM Nova models, and share these valuable insights.

In this post, we demonstrate how to use HAQM Personalize and HAQM 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 HAQM 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 HAQM Bedrock Knowledge Bases and HAQM 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 HAQM 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 HAQM 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.

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 […]

In this post, we explore how to use HAQM 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).

This post demonstrates how HAQM Bedrock Knowledge Bases can help you scale your data management effectively while maintaining proper access controls on different management levels.

Prompt caching, now generally available on HAQM Bedrock with Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet, along with Nova Micro, Nova Lite, and Nova Pro models, lowers response latency by up to 85% and reduces costs up to 90% by caching frequently used prompts across multiple API calls. This post provides a detailed overview of the prompt caching feature on HAQM Bedrock and offers guidance on how to effectively use this feature to achieve improved latency and cost savings.

In this blog, we discuss how to optimize prompting in HAQM Nova for the best price-performance.