AWS Machine Learning Blog

Category: Best Practices

How Salesforce achieves high-performance model deployment with HAQM SageMaker AI

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Model Serving team is working to push the boundaries of natural language processing and AI capabilities for enterprise applications. Their key focus areas include optimizing large […]

Solution Overview

Clario enhances the quality of the clinical trial documentation process with HAQM Bedrock

The collaboration between Clario and AWS demonstrated the potential of AWS AI and machine learning (AI/ML) services and generative AI models, such as Anthropic’s Claude, to streamline document generation processes in the life sciences industry and, specifically, for complicated clinical trial processes.

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 Lumi streamlines loan approvals with HAQM SageMaker AI

Lumi is a leading Australian fintech lender empowering small businesses with fast, flexible, and transparent funding solutions. They use real-time data and machine learning (ML) to offer customized loans that fuel sustainable growth and solve the challenges of accessing capital. This post explores how Lumi uses HAQM SageMaker AI to meet this goal, enhance their transaction processing and classification capabilities, and ultimately grow their business by providing faster processing of loan applications, more accurate credit decisions, and improved customer experience.

Minimize generative AI hallucinations with HAQM Bedrock Automated Reasoning checks

To improve factual accuracy of large language model (LLM) responses, AWS announced HAQM 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 HAQM Bedrock Automated Reasoning checks.

Retrieval vs. generation metrics

Evaluate and improve performance of HAQM Bedrock Knowledge Bases

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.