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

AWS Machine Learning

The player data was used to derive features for model development: X – Player position along the long axis of the field Y – Player position along the short axis of the field S – Speed in yards/second; replaced by Dis*10 to make it more accurate (Dis is the distance in the past 0.1 k10 Baseline 0 4.074 9.62 between the two distributions.

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Sunshine Financial ServiceCommitted to Excellence

COPC

Calibrate QAs using a quantified approach. Establish structured analysis and improvement processes based on customer feedback. Measure CSAT and DSAT at the program level. Use DSAT insights to identify key drivers, track results and create action plans. Interested in exploring COPC Certification or one of our other Virtual CX Services ?

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What to do with a ‘Watermelon Customer’?

CustomerSuccessBox

This data can uncover the underlying intent of the customer. Moreover, the rule engines are not calibrated frequently and as result the signals are false. advanced Artificial intelligence (AI ) and big data analysis model, it learns once and keeps learning with every new renewal, upsells, and even churn. Powered by its.

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How Artificial Intelligence Impacts the Contact Center

The Northridge Group

AI is a powerful tool that can be leveraged to improve customer service and efficiency, but it must also be monitored, tuned, and calibrated so that it doesn’t lead clients and service awry. What are the downsides/limits of Artificial Intelligence?

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Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

AWS Machine Learning

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data.

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Rule-Based Automation Is Out: Intelligent Automation Is In!

SmartKarrot

Interpret big data. Industries collect mounds and mounds of data in a single day. Even if an average human being works at maximum efficiency, it would be a task to siphon through and make sense of all boundless big data. It also involves a tremendous amount of data collection, collation, and analysis.