AWS Machine Learning Blog

Category: Application Services

Build a multi-tenant generative AI environment for your enterprise on AWS

While organizations continue to discover the powerful applications of generative AI, adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. In the first part of the series, we showed how AI administrators can build a […]

High-level design of the solution

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS offers powerful generative AI services, including Amazon Bedrock, which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. Many businesses want to integrate these cutting-edge AI capabilities with their existing collaboration tools, such as Google Chat, to […]

Deploy a serverless web application to edit images using Amazon Bedrock

In this post, we explore a sample solution that you can use to deploy an image editing application by using AWS serverless services and generative AI services. We use Amazon Bedrock and an Amazon Titan FM that allow you to edit images by using prompts.

Summarize call transcriptions securely with Amazon Transcribe and Amazon Bedrock Guardrails

Summarize call transcriptions securely with Amazon Transcribe and Amazon Bedrock Guardrails

In this post, we show you how to use Amazon Transcribe to get near real-time transcriptions of calls sent to Amazon Bedrock for summarization and sensitive data redaction. We’ll walk through an architecture that uses AWS Step Functions to orchestrate the process, providing seamless integration and efficient processing

Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

In this post, we show how to create a multimodal chat assistant on Amazon Web Services (AWS) using Amazon Bedrock models, where users can submit images and questions, and text responses will be sourced from a closed set of proprietary documents.

Improve employee productivity using generative AI with Amazon Bedrock

Improve employee productivity using generative AI with Amazon Bedrock

In this post, we show you the Employee Productivity GenAI Assistant Example, a solution built on AWS technologies like Amazon Bedrock, to automate writing tasks and enhance employee productivity.

Generative AI-powered American Sign Language avatars

GenASL: Generative AI-powered American Sign Language avatars

In this post, we dive into the architecture and implementation details of GenASL, which uses AWS generative AI capabilities to create human-like ASL avatar videos. GenASL is a solution that translates speech or text into expressive ASL avatar animations, bridging the gap between spoken and written language and sign language.

Automating model customization in Amazon Bedrock with AWS Step Functions workflow

Large language models have become indispensable in generating intelligent and nuanced responses across a wide variety of business use cases. However, enterprises often have unique data and use cases that require customizing large language models beyond their out-of-the-box capabilities. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) […]

Build your multilingual personal calendar assistant with Amazon Bedrock and AWS Step Functions

This post shows you how to apply AWS services such as Amazon Bedrock, AWS Step Functions, and Amazon Simple Email Service (Amazon SES) to build a fully-automated multilingual calendar artificial intelligence (AI) assistant. It understands the incoming messages, translates them to the preferred language, and automatically sets up calendar reminders.

Create an end-to-end serverless digital assistant for semantic search with Amazon Bedrock

With the rise of generative artificial intelligence (AI), an increasing number of organizations use digital assistants to have their end-users ask domain-specific questions, using Retrieval Augmented Generation (RAG) over their enterprise data sources. As organizations transition from proofs of concept to production workloads, they establish objectives to run and scale their workloads with minimal operational […]