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Cutting Through the Buzzwords of AI in the Contact Center

CCNG

Analytical AI is the fuel that drives the AI engine for contact centers. Implementing one solution at a time allows for proper calibration of that solution and gives you the ability to feel the full ramifications of that technology without any guesswork. The more information you feed it, the better your operations will become.

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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Import intel extensions for PyTorch to help with quantization and optimization and import torch for array manipulations: import intel_extension_for_pytorch as ipex import torch Apply model calibration for 100 iterations. Solutions Architect in the Strategic Accounts team at AWS. About the Authors Rohit Chowdhary is a Sr.

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

AWS Machine Learning

Data preprocessing and feature engineering First, the tracking data was filtered for just the data related to punts and kickoff returns. The data preprocessing and feature engineering was adapted from the winner of the NFL Big Data Bowl competition on Kaggle. The data distribution for punt and kickoff are different.

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

AWS Machine Learning

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. We now carry out feature engineering steps and then fit the model. The solution outlined in the post is part of SageMaker JumpStart.

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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

Based on 10 years of historical data, hundreds of thousands of face-offs were used to engineer over 70 features fed into the model to provide real-time probabilities. By continuously listening to NHL’s expertise and testing hypotheses, AWS’s scientists engineered over 100 features that correlate to the face-off event.

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Paradigm shift in Customer Success technology with AI

CustomerSuccessBox

Knowing what is the best action plan to drive customer success for each account takes years of experience and understanding. Traditional Customer Success software works on a (now obsolete) rule-based engine to generate any early warning signals. In a new field where experienced CSM are hard to find. CSMs on all past renewals.

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What to do with a ‘Watermelon Customer’?

CustomerSuccessBox

Now, the concern here is that as a CSM, you could easily overlook a ‘green’ customer account thinking it to be a healthy one! Possibly, it can present a more accurate picture of the account’s health. Moreover, the rule engines are not calibrated frequently and as result the signals are false. Based on rule engines.