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

AWS Machine Learning

Before you get started, refer to Part 1 for a high-level overview of the insurance use case with IDP and details about the data capture and classification stages. 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.

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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Table formats provide a way to abstract data files as a table. You can also use the FeatureGroup().put_record

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

We demonstrate CDE using simple examples and provide a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account. After ingestion, images can be searched via the Amazon Kendra search console, API, or SDK. However, we can use CDE for a wider range of use cases.

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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture.

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Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

AWS Machine Learning

Finally, we show how you can integrate this car pose detection solution into your existing web application using services like Amazon API Gateway and AWS Amplify. For each option, we host an AWS Lambda function behind an API Gateway that is exposed to our mock application. Aamna Najmi is a Data Scientist with AWS Professional Services.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

In addition to awareness, your teams should take action to account for generative AI in governance, assurance, and compliance validation practices. You should begin by extending your existing security, assurance, compliance, and development programs to account for generative AI.

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Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning

This example notebook demonstrates the pattern of using Feature Store as a central repository from which data scientists can extract training datasets. In addition to creating a training dataset, we use the PutRecord API to put the 1-week feature aggregations into the online feature store nightly. Nov-01,22:01:00 1 74.99 …9843 99.50