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Cutting Through the Buzzwords of AI in the Contact Center

CCNG

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. AI can also anticipate a customer’s next ask or step based on historical data at the account level as well as the overall global level.

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Building Your Best Culture in 2019

CX Accelerator

The specific challenge was as follows: How can we as leaders develop a healthy blend of compassion, flexibility, and empathy, while fostering the right amount of individual accountability inside of our teams? From Jeremy Hyde: How do your leaders calibrate on what they “coach” on and how/when they approach it?

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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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Adapting to a Global Workforce: Managing Remote Teams Effectively

Playvox

Leverage a Workforce Management Solution When you have a globally dispersed team, it becomes even more important to leverage a solution that accounts for differing schedules and skill sets. Let’s talk about solutions and best practices you can leverage to set your remote teams up for success.

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Putting Humanity in Contact Centers

Customer Relationship Metrics

Ownership over Accountability. When your focus is on how to hold people accountable, it takes your focus off an important question: “Why do we need to hold people accountable in the first place?”. She states that if you believe people need to be held accountable, what is YOUR underlying belief? Why is that? Ownership.

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

AWS Machine Learning

Furthermore, we looked at the probability of a touchdown and probability plots to evaluate calibration. 9.621 47.519 0.265 The following plot of the observed frequencies and predicted probabilities indicates a good calibration of our best model, with an RMSE of 0.27 k10 Baseline 0 4.074 9.62 47.585 0.306 k10 Baseline 5 4.075 9.626 47.43

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COPC Standards Committee Update: AI in Focus 

COPC

Regular calibration of AI assessment tools to a set standard is necessary for maintaining their effectiveness. A review of new ways AI impacts CX operations and the potential for new metrics noted a lack of measures for AI training and calibration. Is there a need for calibration among those that assess and tune the performance of AI?