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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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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. Detect fraudulent insurance claims.

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More Than Just Number-Crunchers: How Accountants Provide Value-Added Services

Method:CRM

Those poor accountants. In fact, today’s accountants are far more than just number-crunchers — they’re leaders, strategists, technologists, advisors and business specialists. The accounting industry: (p)art of the deal. Accountants speak the language of business. For instance, look at large accounting organizations.

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

AWS Machine Learning

Depending on the design of your feature groups and their scale, you can experience training query performance improvements of 10x to 100x by using this new capability. The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. Creating feature groups using Iceberg table format.

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Scheduling Software for Call Centers: Buying Tips & Best Practices

Callminer

Integrate wider analytics tools into your scheduling solutions for better operational insights. “With integrated analytics software you’ll be able to better forecast agent numbers. Analytics data will be able to show you things like call volume trends, topics of calls, quality of calls and more. .”

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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.