AWS Machine Learning Blog
Connect SharePoint Online to Amazon Q Business usi...
In this post, we explore how to integrate Amazon Q Business with SharePoint Online using the OAuth 2.0 ROPC flow authentication method. We provide both manual and automated approaches using PowerShell scripts for configuring the required Azure AD settings....
John Snow Labs Medical LLMs are now available in A...
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...
Accelerating Mixtral MoE fine-tuning on Amazon Sag...
In this post, we demonstrate how you can address the challenges of model customization being complex, time-consuming, and often expensive by using fully managed environment with Amazon SageMaker Training jobs to fine-tune the Mixtral 8x7B model using PyTorch Fully...
Amazon SageMaker Inference now supports G6e instan...
G6e instances on SageMaker unlock the ability to deploy a wide variety of open source models cost-effectively. With superior memory capacity, enhanced performance, and cost-effectiveness, these instances represent a compelling solution for organizations looking to deploy and scale their...
Orchestrate generative AI workflows with Amazon Be...
This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions. We also touch on the usage...
Build generative AI applications on Amazon Bedrock...
In this post, we demonstrate how to use Amazon Bedrock with the AWS SDK for Python (Boto3) to programmatically incorporate FMs. We explore invoking a specific FM and processing the generated text, showcasing the potential for developers to use...
Improve factual consistency with LLM Debates
In this post, we demonstrate the potential of large language model (LLM) debates using a supervised dataset with ground truth. In this post, we navigate the LLM debating technique with persuasive LLMs having two expert debater LLMs (Anthropic Claude...
Governing the ML lifecycle at scale, Part 3: Setti...
This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a...
Amazon Bedrock Flows is now generally available wi...
Today, we are excited to announce the general availability of Amazon Bedrock Flows (previously known as Prompt Flows). With Bedrock Flows, you can quickly build and execute complex generative AI workflows without writing code. Bedrock Flows makes it easier...
Implement secure API access to your Amazon Q Busin...
Amazon Q Business provides a rich set of APIs to perform administrative tasks and to build an AI assistant with customized user experience for your enterprise. In this post, we show how to use Amazon Q Business APIs when...
Enhance speech synthesis and video generation mode...
In this post, we show you how to implement an audio and video segmentation solution using SageMaker Ground Truth. We guide you through deploying the necessary infrastructure using AWS CloudFormation, creating an internal labeling workforce, and setting up your...
Using responsible AI principles with Amazon Bedroc...
In this post, we explore a practical, cost-effective approach for incorporating responsible AI guardrails into Amazon Bedrock Batch Inference workflows. Although we use a call center’s transcript summarization as our primary example, the methods we discuss are broadly applicable...
Revolutionizing knowledge management: VW’s AI prot...
we’re excited to share the journey of the VW—an innovator in the automotive industry and Europe’s largest car maker—to enhance knowledge management by using generative AI, Amazon Bedrock, and Amazon Kendra to devise a solution based on Retrieval Augmented...
Fine-tune large language models with Amazon SageMa...
Fine-tuning foundation models (FMs) is a process that involves exposing a pre-trained FM to task-specific data and fine-tuning its parameters. It can then develop a deeper understanding and produce more accurate and relevant outputs for that particular domain. In...
Unify structured data in Amazon Aurora and unstruc...
In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query...
Automate Q&A email responses with Amazon Bedrock K...
In this post, we illustrate automating the responses to email inquiries by using Amazon Bedrock Knowledge Bases and Amazon Simple Email Service (Amazon SES), both fully managed services. By linking user queries to relevant company domain information, Amazon Bedrock...
Streamline RAG applications with intelligent metad...
In this post, we explore an innovative approach that uses LLMs on Amazon Bedrock to intelligently extract metadata filters from natural language queries. By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract...
Embedding secure generative AI in mission-critical...
This post shows how Mark43 uses Amazon Q Business to create a secure, generative AI-powered assistant that drives operational efficiency and improves community service. We explain how they embedded Amazon Q Business web experience in their web application with low...