AWS Machine Learning Blog

Generative AI has transformed customer support, offering businesses the ability to respond faster, more accurately, and with greater personalization. AI agents, powered by large language models (LLMs), can analyze complex customer inquiries, access multiple data sources, and deliver relevant, detailed responses. In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data […]

In this post, we share how Hearst, one of the nation’s largest global, diversified information, services, and media companies, overcame these challenges by creating a self-service generative AI conversational assistant for business units seeking guidance from their CCoE.

The rise of large language models (LLMs) and foundation models (FMs) has revolutionized the field of natural language processing (NLP) and artificial intelligence (AI). These powerful models, trained on vast amounts of data, can generate human-like text, answer questions, and even engage in creative writing tasks. However, training and deploying such models from scratch is […]

AWS provides a powerful set of tools and services that simplify the process of building and deploying generative AI applications, even for those with limited experience in frontend and backend development. In this post, we explore a practical solution that uses Streamlit, a Python library for building interactive data applications, and AWS services like Amazon Elastic Container Service (Amazon ECS), Amazon Cognito, and the AWS Cloud Development Kit (AWS CDK) to create a user-friendly generative AI application with authentication and deployment.

In this post, we examine how to create business value through speech analytics with some examples focused on the following: 1) automatically summarizing, categorizing, and analyzing marketing content such as podcasts, recorded interviews, or videos, and creating new marketing materials based on those assets, 2) automatically extracting key points, summaries, and sentiment from a recorded meeting (such as an earnings call), and 3) transcribing and analyzing contact center calls to improve customer experience.

In this post, we explore the best practices and lessons learned for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock. We discuss the important components of fine-tuning, including use case definition, data preparation, model customization, and performance evaluation.

Amazon Bedrock has launched a capability that organizations can use to tag on-demand models and monitor associated costs. Organizations can now label all Amazon Bedrock models with AWS cost allocation tags, aligning usage to specific organizational taxonomies such as cost centers, business units, and applications.

In this post, we discuss how to use AWS to support a decentralized clinical trial across the four main pillars of a decentralized clinical trial (virtual trials, personalized patient engagement, patient-centric trial design, and centralized data management). By exploring these AWS powered alternatives, we aim to demonstrate how organizations can drive progress towards more environmentally friendly clinical research practices.

This post covers the steps to configure the Amazon Q Business Google Drive connector, including authentication setup and verifying the secure indexing of your Google Drive content.

Druva enables cyber, data, and operational resilience for thousands of enterprises, and is trusted by 60 of the Fortune 500. In this post, we show how Druva approached natural language querying (NLQ)—asking questions in English and getting tabular data as answers—using Amazon Bedrock, the challenges they faced, sample prompts, and key learnings.