Category: Amazon Machine Learning

In this post, we show you how to use Amazon Bedrock to automate email responses to customer queries. With our solution, you can identify the intent of customer emails and send an automated response if the intent matches your existing knowledge base or data sources. If the intent doesn’t have a match, the email goes to the support team for a manual response.

In this post, we demonstrate how to seamlessly automate the deployment of an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and the AWS Cloud Development Kit (AWS CDK), enabling organizations to quickly set up a powerful question answering system.

Today, we are happy to announce the general availability of cross-region inference, a powerful feature allowing automatic cross-region inference routing for requests coming to Amazon Bedrock. This offers developers using on-demand inference mode, a seamless solution for managing optimal availability, performance, and resiliency while managing incoming traffic spikes of applications powered by Amazon Bedrock. By opting in, developers no longer have to spend time and effort predicting demand fluctuations.

In this post, we will harness the power of generative artificial intelligence (AI) and Amazon Bedrock to help organizations simplify and effectively manage remediations of AWS Security Hub control findings.

In this post, we discuss existing prompt-level threats and outline several security guardrails for mitigating prompt-level threats. For our example, we work with Anthropic Claude on Amazon Bedrock, implementing prompt templates that allow us to enforce guardrails against common security threats such as prompt injection. These templates are compatible with and can be modified for other LLMs.

In this post, we introduce the new Amazon Titan Text Premier model, specifically optimized for enterprise use cases, such as building Retrieval Augmented Generation (RAG) and agent-based applications. Such integrations enable advanced applications like building interactive AI assistants that use enterprise APIs and interact with your propriety documents.

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.

Through this series of posts, we share our generative AI journey and use cases, detailing the architecture, AWS services used, lessons learned, and the impact of these solutions on our teams and customers. In this first post, we explore Account Summaries, one of our initial production use cases built on Amazon Bedrock. Account Summaries equips our teams to be better prepared for customer engagements. It combines information from various sources into comprehensive, on-demand summaries available in our CRM or proactively delivered based on upcoming meetings. From the period of September 2023 to March 2024, sellers leveraging GenAI Account Summaries saw a 4.9% increase in value of opportunities created.

In this post, we describe the development of the automated summary feature in Verisk’s Discovery Navigator incorporating generative AI, the data, the architecture, and the evaluation of the pipeline. This new functionality offers an immediate overview of the initial injury and current medical status, empowering record reviewers of all skill levels to quickly assess injury severity with the click of a button.

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators.