AWS Machine Learning Blog

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.

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 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.

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).