AWS Machine Learning Blog

Category: Amazon Machine Learning

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Enhance your customer’s omnichannel experience with Amazon Bedrock and Amazon Lex

In this post, we show you how to set up Amazon Lex for an omnichannel chatbot experience and Amazon Bedrock to be your secondary validation layer. This allows your customers to potentially provide out-of-band responses both at the intent and slot collection levels without having to be re-prompted, allowing for a seamless customer experience.

Introducing multi-turn conversation with an agent node for Amazon Bedrock Flows (preview)

Today, we’re excited to announce multi-turn conversation with an agent node (preview), a powerful new capability in Flows. This new capability enhances the agent node functionality, enabling dynamic, back-and-forth conversations between users and flows, similar to a natural dialogue in a flow execution.

Video security analysis for privileged access management using generative AI and Amazon Bedrock

In this post, we show you an innovative solution to a challenge faced by security teams in highly regulated industries: the efficient security analysis of vast amounts of video recordings from Privileged Access Management (PAM) systems. We demonstrate how you can use Anthropic’s Claude 3 family of models and Amazon Bedrock to perform the complex task of analyzing video recordings of server console sessions and perform queries to highlight any potential security anomalies.

How Cato Networks uses Amazon Bedrock to transform free text search into structured GraphQL queries

Accurately converting free text inputs into structured data is crucial for applications that involve data management and user interaction. In this post, we introduce a real business use case from Cato Networks that significantly improved user experience. By using Amazon Bedrock, we gained access to state-of-the-art generative language models with built-in support for JSON schemas and structured data.

HCLTech’s AWS powered AutoWise Companion: A seamless experience for informed automotive buyer decisions with data-driven design

This post introduces HCLTech’s AutoWise Companion, a transformative generative AI solution designed to enhance customers’ vehicle purchasing journey. In this post, we analyze the current industry challenges and guide readers through the AutoWise Companion solution functional flow and architecture design using built-in AWS services and open source tools. Additionally, we discuss the design from security and responsible AI perspectives, demonstrating how you can apply this solution to a wider range of industry scenarios.

Implement RAG while meeting data residency requirements using AWS hybrid and edge services

In this post, we show how to extend Amazon Bedrock Agents to hybrid and edge services such as AWS Outposts and AWS Local Zones to build distributed Retrieval Augmented Generation (RAG) applications with on-premises data for improved model outcomes. With Outposts, we also cover a reference pattern for a fully local RAG application that requires both the foundation model (FM) and data sources to reside on premises.

Unlocking complex problem-solving with multi-agent collaboration on Amazon Bedrock

The research team at AWS has worked extensively on building and evaluating the multi-agent collaboration (MAC) framework so customers can orchestrate multiple AI agents on Amazon Bedrock Agents. In this post, we explore the concept of multi-agent collaboration (MAC) and its benefits, as well as the key components of our MAC framework. We also go deeper into our evaluation methodology and present insights from our studies.

How BQA streamlines education quality reporting using Amazon Bedrock

The Education and Training Quality Authority (BQA) plays a critical role in improving the quality of education and training services in the Kingdom Bahrain. BQA reviews the performance of all education and training institutions, including schools, universities, and vocational institutes, thereby promoting the professional advancement of the nation’s human capital. In this post, we explore how BQA used the power of Amazon Bedrock, Amazon SageMaker JumpStart, and other AWS services to streamline the overall reporting workflow.

Build an Amazon Bedrock based digital lending solution on AWS

In this post, we propose a solution using DigitalDhan, a generative AI-based solution to automate customer onboarding and digital lending. The proposed solution uses Amazon Bedrock Agents to automate services related to KYC verification, credit and risk assessment, and notification. Financial institutions can use this solution to help automate the customer onboarding, KYC verification, credit decisioning, credit underwriting, and notification processes.