Beyond Point Estimates: Deploying ML Models With Statistical Confidence

Most ML teams deploy models based on raw metric comparisons without quantifying uncertainty, making deployment decisions essentially blind to sampling variability. reliably-metrics automates confidence interval computation and statistical significance testing, transforming model evaluation from guesswork into rigorous science. This ensures teams can distinguish genuine improvements from noise, reducing the risk of shipping inferior models to production.