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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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LiDAR 3D point cloud labeling with Velodyne LiDAR sensor in Amazon SageMaker Ground Truth

AWS Machine Learning

To implement the solution in this post, you must have the following prerequisites: An AWS account for running the code. With a combination of optimal power and high performance, this sensor provides distance and calibrated reflectivity measurements at all rotational angles. LiDAR vehicle calibration. Completing a labeling job.

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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Smitha obtained her license as CPA in 2007 from the California Board of Accountancy. With more than 15 years of experience in business, finance and accounting, she is also responsible for implementing financial controls and processes. We’ve had success in increasing efficiency of contact centers by…”.

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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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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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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.