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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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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.

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4 Ways Banks Can Hyper-Personalize Customer Experiences at Scale

SharpenCX

Using data to get to know your customers Banks that regularly utilize their data analytics to optimize customer experiences see a growth rate of 3.2x Once you’re able to aggregate your data, there are a number of ways that banks can use data analytics to personalize customer experiences. faster than their competitors. Remember, A.B.T.,

Banking 76
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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")

Scripts 96
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How to Increase Agent Productivity: 12 Expert-Approved Tips + Strategies

JustCall

Regular feedback can also foster a sense of accountability and improve job satisfaction, which can, in turn, increase motivation and engagement. To be effective, feedback and coaching should be specific, timely, and delivered in a constructive and supportive manner.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning

At Deutsche Bahn, a dedicated AI platform team manages and operates the SageMaker Studio platform, and multiple data analytics teams within the organization use the platform to develop, train, and run various analytics and ML activities. For high availability, multiple identical private isolated subnets are provisioned.

APIs 100