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Dev.to
6/15/2026
Stop Shipping ML Models With Bare Floats: A Deep Dive Into Statistically Rigorous Model Evaluation

Stop Shipping ML Models With Bare Floats: A Deep Dive Into Statistically Rigorous Model Evaluation

Short summary

Most ML teams ship model updates based on point-estimate metric differences without quantifying uncertainty. reliably-metrics automates confidence interval computation and significance testing, eliminating guesswork from deployment decisions. The library also covers calibration analysis, visualization, and reproducibility with explicit seeds.

  • •Point estimates (0.847 vs 0.851 AUROC) hide uncertainty and lead to poor deployment decisions
  • •reliably-metrics adds 95% confidence intervals, p-values, and statistical significance testing automatically
  • •Supports calibration analysis, disentanglement metrics, reproducible seeding, and HTML reporting

Generated with AI, which can make mistakes.

#ai-tools#research-breakthrough#open-source
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