Machine Learning: 5 Additional Metrics to Avoid High‑Performing but Useless Models
summary
Describes five practical production-readiness checks for machine learning: measure latency and throughput and prefer the faster model even if its classification metrics are slightly worse; translate model improvements into business units (e.g., euros saved rather than just fraud count); quantify maintenance complexity (feature engineering, aggregations, disk size, library dependencies) and compute footprint (parameters, FLOPs, energy); and add global and local explainability using CHAP to build stakeholder trust and detect learned shortcuts.
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chapters (10)
- 0:00Introduction
- 1:04Data scientist vs developer
- 2:09Performance vs speed
- 3:24Align metrics with business
- 4:50Model maintenance complexity
- 6:57Balance complexity and gains
- 7:39Model environmental impact
- 9:01Convincing decision makers
- 11:29ML as production software
- 11:52Q&A
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