Remove Calibration Remove How To Remove Metrics Remove Scripts
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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

In this post, we show you how to build and deploy INT8 inference with your own processing container for PyTorch. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. py scripts for testing. Refer to invoke-INT8.py

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Quality Time for Your Contact Center?

Monet Software

3 Calibrate Quality Evaluations and Metrics. Central to the QM function is the evaluation of contacts for regulatory compliance, adherence to scripting and qualitative features like professionalism, product knowledge and empathy. That doesn’t mean SMB call centers should give up on accurate measurement, though!

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

AWS Machine Learning

We show you how to train, deploy and use a churn prediction model that has processed numerical, categorical, and textual features to make its prediction. For more information on specific solutions under each use case and how to launch a JumpStart solution, see Solution Templates. Solution overview. BERT + Random Forest.

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Putting Humanity in Contact Centers

Customer Relationship Metrics

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?”. Organizations must create performance management and employee development programs that use customer relationship metrics to drive their service delivery.

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

Callminer

Metrics, Measure, and Monitor – Make sure your metrics and associated goals are clear and concise while aligning with efficiency and effectiveness. Make each metric public and ensure everyone knows why that metric is measured. Interactive agent scripts from Zingtree solve this problem. Bill Dettering.

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Detect fraudulent transactions using machine learning with Amazon SageMaker

AWS Machine Learning

In this post, we show you how to build a dynamic, self-improving, and maintainable credit card fraud detection system with machine learning (ML) using Amazon SageMaker. Lastly, we compare the classification result with the ground truth labels and compute the evaluation metrics. This time we can also calculate the ROC AUC metric.

APIs 67
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101 Call Monitoring Parameters for Quality and Coaching

Voxjar

When you experiment with different metrics and track improvement over time, you set yourself up for success. Good parameters are measurable and clearly defined (something you can test through calibration sessions with management, supervisors, and reps – post on this coming soon). Did your rep: Follow the greeting script.