Category: Amazon Machine Learning

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime) to a centralized video insights and summarization engine. This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. The solution notes the logged actions per individual and provides suggested actions for the uploader. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.

This post demonstrates how to build an IDP pipeline for automatically extracting and processing data from documents using Amazon Bedrock Prompt Flows, a fully managed service that enables you to build generative AI workflow using Amazon Bedrock and other services in an intuitive visual builder. Amazon Bedrock Prompt Flows allows you to quickly update your pipelines as your business changes, scaling your document processing workflows to help meet evolving demands.

In this post, we explore how Planview was able to develop a generative AI assistant to address complex work management process by adopting Amazon Bedrock.

In this post, we cover the core concepts behind RAG architectures and discuss strategies for evaluating RAG performance, both quantitatively through metrics and qualitatively by analyzing individual outputs. We outline several practical tips for improving text retrieval, including using hybrid search techniques, enhancing context through data preprocessing, and rewriting queries for better relevance.

Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models (LLM) as their reasoning engine or brain. In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant.

In this post, we learn how SnapLogic’s Agent Creator leverages Amazon Bedrock to provide a low-code platform that enables enterprises to quickly develop and deploy powerful generative AI applications without deep technical expertise.

In this post, we demonstrate how to use Amazon Bedrock to create synthetic data, fine-tune a BAAI General Embeddings (BGE) model, and deploy it using Amazon SageMaker.

In this post, we demonstrate how to deploy a contextual AI assistant. We build a solution which provides users with a familiar and convenient interface using Amazon Bedrock Knowledge Bases, Amazon Lex, and Amazon Connect, with WhatsApp as the channel.

In this post, we guide you through a step-by-step process to seamlessly migrate and safeguard your SageMaker domain from one active Region to another passive or active Region, including all associated user profiles and files.

We’re pleased to announce the general availability (GA) of Amazon Bedrock Custom Model Import. This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API.