AWS Machine Learning Blog
Category: Partner solutions
Improving Retrieval Augmented Generation accuracy with GraphRAG
Lettria, an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.
Real value, real time: Production AI with Amazon SageMaker and Tecton
In this post, we discuss how Amazon SageMaker and Tecton work together to simplify the development and deployment of production-ready AI applications, particularly for real-time use cases like fraud detection. The integration enables faster time to value by abstracting away complex engineering tasks, allowing teams to focus on building features and use cases while providing a streamlined framework for both offline training and online serving of ML models.
Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker
Today, we’re excited to announce that AI apps from AWS Partners are now available in SageMaker. You can now find, deploy, and use these AI apps privately and securely, all without leaving SageMaker AI, so you can develop performant AI models faster.
Enhanced observability for AWS Trainium and AWS Inferentia with Datadog
This post walks you through Datadog’s new integration with AWS Neuron, which helps you monitor your AWS Trainium and AWS Inferentia instances by providing deep observability into resource utilization, model execution performance, latency, and real-time infrastructure health, enabling you to optimize machine learning (ML) workloads and achieve high-performance at scale.
John Snow Labs Medical LLMs are now available in Amazon SageMaker JumpStart
Today, we are excited to announce that John Snow Labs’ Medical LLM – Small and Medical LLM – Medium large language models (LLMs) are now available on Amazon SageMaker Jumpstart. For medical doctors, this tool provides a rapid understanding of a patient’s medical journey, aiding in timely and informed decision-making from extensive documentation. This summarization capability not only boosts efficiency but also makes sure that no critical details are overlooked, thereby supporting optimal patient care and enhancing healthcare outcomes.
Accelerate performance using a custom chunking mechanism with Amazon Bedrock
This post explores how Accenture used the customization capabilities of Knowledge Bases for Amazon Bedrock to incorporate their data processing workflow and custom logic to create a custom chunking mechanism that enhances the performance of Retrieval Augmented Generation (RAG) and unlock the potential of your PDF data.
Harness the power of AI and ML using Splunk and Amazon SageMaker Canvas
For organizations looking beyond the use of out-of-the-box Splunk AI/ML features, this post explores how Amazon SageMaker Canvas, a no-code ML development service, can be used in conjunction with data collected in Splunk to drive actionable insights. We also demonstrate how to use the generative AI capabilities of SageMaker Canvas to speed up your data exploration and help you build better ML models.
Cepsa Química improves the efficiency and accuracy of product stewardship using Amazon Bedrock
In this post, we explain how Cepsa Química and partner Keepler have implemented a generative AI assistant to increase the efficiency of the product stewardship team when answering compliance queries related to the chemical products they market. To accelerate development, they used Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy and safety.
Build generative AI–powered Salesforce applications with Amazon Bedrock
In this post, we show how native integrations between Salesforce and Amazon Web Services (AWS) enable you to Bring Your Own Large Language Models (BYO LLMs) from your AWS account to power generative artificial intelligence (AI) applications in Salesforce. Requests and responses between Salesforce and Amazon Bedrock pass through the Einstein Trust Layer, which promotes responsible AI use across Salesforce.
How Deloitte Italy built a digital payments fraud detection solution using quantum machine learning and Amazon Braket
As digital commerce expands, fraud detection has become critical in protecting businesses and consumers engaging in online transactions. Implementing machine learning (ML) algorithms enables real-time analysis of high-volume transactional data to rapidly identify fraudulent activity. This advanced capability helps mitigate financial risks and safeguard customer privacy within expanding digital markets. Deloitte is a strategic global […]