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Call Center Quality Assurance: 8 Common Challenges and How to Overcome Them

Balto

Regular reviews ensure that quality benchmarks are being met and provide valuable feedback for continuous improvement in customer interactions. Regular calibration sessions with QA evaluators help ensure consistency and alignment across the team. Solution: Balance constructive criticism with positive reinforcement.

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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

We also share the key technical challenges that were solved during construction of the Face-off Probability model. At the end, we found that the LightGBM model worked best with well-calibrated accuracy metrics. How it works. Imagine the following scenario: It’s a tie game between two NHL teams that will determine who moves forward.

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Evaluate the text summarization capabilities of LLMs for enhanced decision-making on AWS

AWS Machine Learning

The overall goal of this post is to demystify summarization evaluation to help teams better benchmark performance on this critical capability as they seek to maximize value. Use it as a baseline or benchmark for summary quality related to content selection. ROUGE would not identify these issues. and expects a response from the model.

Metrics 131
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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

Each trained model needs to be benchmarked against many tasks not only to assess its performances but also to compare it with other existing models, to identify areas that needs improvements and finally, to keep track of advancements in the field. Evaluating these models allows continuous model improvement, calibration and debugging.

Benchmark 108