Traditional metrics like accuracy, precision, and recall only scratch the surface of true model performance. Today’s businesses need metrics that reflect real-world complexity and strategic impact. That’s why I’m introducing:

Model Deployment Reliability (MDR): Measures how well your model holds up in changing conditions—ensuring stability, trust, and long-term success.

Contextual Utility Index (CUI): Connects model performance directly to business value, translating predictions into measurable ROI and strategic outcomes.

https://medium.com/@chandramouliarun/beyond-accuracy-introducing-two-next-generation-metrics-for-real-world-machine-learning-evaluation-b6938db6c676

These metrics empower executives and data teams to align ML with broader organizational goals, driving sustainable innovation and tangible results.

Let’s raise the bar for how we evaluate machine learning!
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