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

AWS Machine Learning

We also look into how to further use the extracted structured information from claims data to get insights using AWS Analytics and visualization services. We highlight on how extracted structured data from IDP can help against fraudulent claims using AWS Analytics services. The following screenshot shows our results.

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Harnessing the Power of Data to Improve First Contact Resolution

The Northridge Group

Authored by Daniel Fenton , Director, Enterprise Accounts and Molly Clark , Senior Director, Operational Analytics. Leveraging data analytics to improve FCR rates is critical for achieving this objective. The post Harnessing the Power of Data to Improve First Contact Resolution appeared first on The Northridge Group.

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

AWS Machine Learning

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. As a baseline, we used the model that won our NFL Big Data Bowl competition on Kaggle.

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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. With AI-powered tools and analytics, it has become easier than ever to build not just one story but customized stories to appear to end-users’ unique tastes and sensibilities.

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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. Conclusion.

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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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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. He works with government, non-profit, and education customers on big data and analytical projects, helping them build solutions using AWS.

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