AWS Machine Learning Blog
Category: Amazon Bedrock
Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra
This blog is co-written with Josh Reini, Shayak Sen and Anupam Datta from TruEra Amazon SageMaker JumpStart provides a variety of pretrained foundation models such as Llama-2 and Mistal 7B that can be quickly deployed to an endpoint. These foundation models perform well with generative tasks, from crafting text and summaries, answering questions, to producing […]
Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain
Generative AI agents are capable of producing human-like responses and engaging in natural language conversations by orchestrating a chain of calls to foundation models (FMs) and other augmenting tools based on user input. Instead of only fulfilling predefined intents through a static decision tree, agents are autonomous within the context of their suite of available […]
Create summaries of recordings using generative AI with Amazon Bedrock and Amazon Transcribe
October 2024: The contents of this post are outdated. Please refer to Summarize call transcriptions securely with Amazon Transcribe and Amazon Bedrock Guardrails for latest solution and code artifacts. Meeting notes are a crucial part of collaboration, yet they often fall through the cracks. Between leading discussions, listening closely, and typing notes, it’s easy for […]
Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock
Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback. When prompted correctly, these models can carry coherent conversations without any task-specific training data. […]
How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot
This post is co-written with Stanislav Yeshchenko from Q4 Inc. Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. We continue to see emerging challenges stemming from the nature of the assortment of datasets available. These datasets are often a mix of numerical and text data, at times structured, […]
Welcome to a New Era of Building in the Cloud with Generative AI on AWS
We believe generative AI has the potential over time to transform virtually every customer experience we know. The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity […]
Easily build semantic image search using Amazon Titan
Digital publishers are continuously looking for ways to streamline and automate their media workflows to generate and publish new content as rapidly as they can, but without foregoing quality. Adding images to capture the essence of text can improve the reading experience. Machine learning techniques can help you discover such images. “A striking image is […]
Introducing the AWS Generative AI Innovation Center’s Custom Model Program for Anthropic Claude
Since launching in June 2023, the AWS Generative AI Innovation Center team of strategists, data scientists, machine learning (ML) engineers, and solutions architects have worked with hundreds of customers worldwide, and helped them ideate, prioritize, and build bespoke solutions that harness the power of generative AI. Customers worked closely with us to prioritize use cases, […]
Automating product description generation with Amazon Bedrock
In today’s ever-evolving world of ecommerce, the influence of a compelling product description cannot be overstated. It can be the decisive factor that turns a potential visitor into a paying customer or sends them clicking off to a competitor’s site. The manual creation of these descriptions across a vast array of products is a labor-intensive […]
How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action
This post was co-written with Greg Benson, Chief Scientist; Aaron Kesler, Sr. Product Manager; and Rich Dill, Enterprise Solutions Architect from SnapLogic. Many customers are building generative AI apps on Amazon Bedrock and Amazon CodeWhisperer to create code artifacts based on natural language. This use case highlights how large language models (LLMs) are able to […]