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Accenture creates a regulatory document authoring solution using AWS generative AI services

AWS Machine Learning - AI

A key part of the submission process is authoring regulatory documents like the Common Technical Document (CTD), a comprehensive standard formatted document for submitting applications, amendments, supplements, and reports to the FDA. The tedious process of compiling hundreds of documents is also prone to errors.

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Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model

AWS Machine Learning - AI

Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Categorizing documents is an important first step in IDP systems.

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning - AI

Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts. In parallel, the AVM layer invokes a Lambda function to generate Terraform code. For creating lambda function, please follow instructions. Access to Amazon Bedrock models.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

This is done using ReAct prompting, which breaks down the task into a series of steps that are processed sequentially: For device metrics checks, we use the check-device-metrics action group, which involves an API call to Lambda functions that then query Amazon Athena for the requested data. It serves as the data source to the knowledge base.

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Dynamic Data Processing Using Serverless Java With Quarkus on AWS Lambda (Part 1)

Dzone - DevOps

For example, traditional data structures in relational databases started to move forward to a new approach that enables to storage and retrieval of key-value and document data structures using NoSQL databases.

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Your Amazon Bedrock-powered insurance agent can assist human agents by creating new claims, sending pending document reminders for open claims, gathering claims evidence, and searching for information across existing claims and customer knowledge repositories. Send a pending documents reminder to the policy holder of claim 2s34w-8x.

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Techniques and approaches for monitoring large language models on AWS

AWS Machine Learning - AI

In this post, we demonstrate a few metrics for online LLM monitoring and their respective architecture for scale using AWS services such as Amazon CloudWatch and AWS Lambda. Amazon Bedrock saves the request and completion (response) in Amazon Simple Storage Service (Amazon S3) as the per configuration of invocation logging.