AWS Machine Learning Blog
Category: Artificial Intelligence
Accuracy evaluation framework for Amazon Q Business
Generative artificial intelligence (AI), particularly Retrieval Augmented Generation (RAG) solutions, are rapidly demonstrating their vast potential to revolutionize enterprise operations. RAG models combine the strengths of information retrieval systems with advanced natural language generation, enabling more contextually accurate and informative outputs. From automating customer interactions to optimizing backend operation processes, these technologies are not just […]
Elevate healthcare interaction and documentation with Amazon Bedrock and Amazon Transcribe using Live Meeting Assistant
Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. Did you know that for every eight hours that office-based physicians have scheduled with patients, they spend more than five hours in the EHR? As a consequence, healthcare practitioners exhibit a pronounced inclination towards conversational intelligence solutions, […]
Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone
Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Amazon DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without the need for extensive coding or […]
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.
Migrate Amazon SageMaker Data Wrangler flows to Amazon SageMaker Canvas for faster data preparation
This post demonstrates how you can bring your existing SageMaker Data Wrangler flows—the instructions created when building data transformations—from SageMaker Studio Classic to SageMaker Canvas. We provide an example of moving files from SageMaker Studio Classic to Amazon Simple Storage Service (Amazon S3) as an intermediate step before importing them into SageMaker Canvas.
Use IP-restricted presigned URLs to enhance security in Amazon SageMaker Ground Truth
While presigned URLs offer a convenient way to grant temporary access to S3 objects, sharing these URLs with people outside of the workteam can lead to unintended access of those objects. To mitigate this risk and enhance the security of SageMaker Ground Truth labeling tasks, we have introduced a new feature that adds an additional layer of security by restricting access to the presigned URLs to the worker’s IP address or virtual private cloud (VPC) endpoint from which they access the labeling task. In this blog post, we show you how to enable this feature, allowing you to enhance your data security as needed, and outline the success criteria for this feature, including the scenarios where it will be most beneficial.
Unlock the power of structured data for enterprises using natural language with Amazon Q Business
In this post, we discuss an architecture to query structured data using Amazon Q Business, and build out an application to query cost and usage data in Amazon Athena with Amazon Q Business. Amazon Q Business can create SQL queries to your data sources when provided with the database schema, additional metadata describing the columns and tables, and prompting instructions. You can extend this architecture to use additional data sources, query validation, and prompting techniques to cover a wider range of use cases.
Cohere Rerank 3 Nimble now generally available on Amazon SageMaker JumpStart
The Cohere Rerank 3 Nimble foundation model (FM) is now generally available in Amazon SageMaker JumpStart. This model is the newest FM in Cohere’s Rerank model series, built to enhance enterprise search and Retrieval Augmented Generation (RAG) systems. In this post, we discuss the benefits and capabilities of this new model with some examples. Overview […]
Perform generative AI-powered data prep and no-code ML over any size of data using Amazon SageMaker Canvas
Amazon SageMaker Canvas now empowers enterprises to harness the full potential of their data by enabling support of petabyte-scale datasets. Starting today, you can interactively prepare large datasets, create end-to-end data flows, and invoke automated machine learning (AutoML) experiments on petabytes of data—a substantial leap from the previous 5 GB limit. With over 50 connectors, […]
Delight your customers with great conversational experiences via QnABot, a generative AI chatbot
QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and Knowledge Bases for Amazon Bedrock, a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. You can now provide contextual information from your private data sources that can be used to create rich, contextual, conversational experiences. In this post, we discuss how to use QnABot on AWS to deploy a fully functional chatbot integrated with other AWS services, and delight your customers with human agent like conversational experiences.