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5 Best Experience Management Metrics

ClearAction

5 Best Experience Management Metrics Lynn Hunsaker. Why are experience management metrics the #1 challenge year after year? This means current experience management metrics are insufficient! Understand how experience management metrics build upon one another, to see where you should focus. So, what’s the solution?

Metrics 62
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CX is a Team Sport: 2 Surprising Views

ClearAction

Wells Fargo and US Bank biz dev teams made phony customer accounts to meet their year-end bonus goal for higher number of accounts per customer than the industry average: PR nightmare, customer churn, higher customer acquisition cost, stock plunge, massive fines.

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Efficient continual pre-training LLMs for financial domains

AWS Machine Learning

BloombergGPT: Philippe Donnet GPT-NeoX: Antonio De Lorenzo, Simone Gambarini, Enrico Zanetti FLAN-T5-XXL: John M Forsyth, Christopher K Peters, {empty string} Input: CEO of Silicon Valley Bank? ETS-DACP-com is similar to DACP with data selection by averaging all three metrics.

Finance 94
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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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Churn prediction using multimodality of text and tabular features with Amazon SageMaker Jumpstart

AWS Machine Learning

Customer churn is a problem faced by a wide range of companies, from telecommunications to banking, where customers are typically lost to competitors. To try out the solution in your own account, make sure that you have the following in place: An AWS account. If you don’t have an account, you can sign up for one.

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MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

AWS Machine Learning

Productionization of robust ML models requires the collaboration of multiple personas, such as data scientists, ML engineers, data engineers, and business stakeholders, under a semi-automate infrastructure following specific operations (MLOps). Solution overview.

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MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and interact; how to separate the concerns, responsibilities, and skills; and how to use AWS services optimally.