Performance Analysis
ML observability enables fast actionable performance information on models deployed in production. While performance analysis techniques vary on a case-by-case basis depending on model type and its use case in the real world, common metrics include: Accuracy, Recall, F-1, MAE, RMSE, and Precision. Performance analysis in an ML observability system ensures that performance has not degraded drastically from when it was trained or when it was initially promoted to production.