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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

With the increasing use of artificial intelligence (AI) and machine learning (ML) for a vast majority of industries (ranging from healthcare to insurance, from manufacturing to marketing), the primary focus shifts to efficiency when building and training models at scale. This is where advanced log analysis comes into play.

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“ID + Selfie” – Improving digital identity verification using AWS

AWS Machine Learning

The COVID-19 global pandemic has accelerated the need to verify and onboard users online across several industries, such as financial services, insurance, and healthcare. The Amazon Rekognition CompareFaces API. For this, we use the Amazon Rekognition CompareFaces API. The default value is NONE. Solution overview.

APIs 73
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Detect real and live users and deter bad actors using Amazon Rekognition Face Liveness

AWS Machine Learning

Financial services, the gig economy, telco, healthcare, social networking, and other customers use face verification during online onboarding, step-up authentication, age-based access restriction, and bot detection. They also estimate the user’s age using facial analysis before allowing access to age-restricted content.

APIs 77
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Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

AWS Machine Learning

Technical challenges with multi-modal data further include the complexity of integrating and modeling different data types, the difficulty of combining data from multiple modalities (text, images, audio, video), and the need for advanced computer science skills and sophisticated analysis tools.

Marketing 100
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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

For a quantitative analysis of the generated impression, we use ROUGE (Recall-Oriented Understudy for Gisting Evaluation), the most commonly used metric for evaluating summarization. Prerequisites To get started, you need an AWS account in which you can use SageMaker Studio.

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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

In Part 1, we saw how to use Amazon Textract APIs to extract information like forms and tables from documents, and how to analyze invoices and identity documents. Extract default entities with the Amazon Comprehend DetectEntities API. The response from the DetectEntities API includes the default entities. Extraction phase.

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Analyze and tag assets stored in Veeva Vault PromoMats using Amazon AppFlow and Amazon AI Services

AWS Machine Learning

In a previous post , we talked about analyzing and tagging assets stored in Veeva Vault PromoMats using Amazon AI services and the Veeva Vault Platform’s APIs. In addition to the no-code interface, Amazon AppFlow supports configuration via API, AWS CLI, and AWS CloudFormation interfaces.

APIs 72