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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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Same Tactics, Different Scripts: What Contact Center Fraud Sounds Like in the Age of Coronavirus

pindrop

With verified account numbers and some basic information, a fraudster has all they need to execute fraud through the phone channel using convincing scripts involving the current crisis to socially engineer contact center agents and individuals. . The New Fraud Scripts. Travel-Related Inconveniences and Emergencies .

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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Account Setup and Verification : Upon receiving a debt, the agency sets up an account for the debtor and verifies all the details. Advanced analytics and machine learning algorithms are also being integrated into call center operations to predict debtor behavior and optimize contact strategies. In the U.S.,

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Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In this post, we address the analytics and ML platform team as a consumer in the data mesh.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models. Reusability – Without reusable MLOps frameworks, each model must be developed and governed separately, which adds to the overall effort and delays model operationalization.

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

AWS Machine Learning

Homomorphic encryption is a new approach to encryption that allows computations and analytical functions to be run on encrypted data, without first having to decrypt it, in order to preserve privacy in cases where you have a policy that states data should never be decrypted. . resource("s3").Bucket Bucket (bucket).Object resource("s3").Bucket(bucket).Object("request.pkl").upload_file("request.pkl")

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