momo-ml 0.1.4

A production-oriented model monitoring library for detecting performance drift, data drift, and prediction drift, with automated reporting for ML systems.

momo-ml

momo-ml (MOdel MOnitoring for ML) is a production‑oriented library for systematically, scalably, and automatically monitoring model quality.
It covers key monitoring dimensions such as performance drift, data drift, and prediction drift, and also supports automatic generation of visual reports.

This project is suitable for data science, ML engineering, MLOps, and other scenarios – it can be integrated into various monitoring and governance workflows after model deployment.

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📌 Features

1. Performance Drift

Monitor changes in model prediction performance over different time windows:

• Metrics: AUC / F1 / Precision / Recall / RMSE, etc.
• Compare reference window vs. current window.
• Rolling window trend analysis.

2. Data Drift

Detect shifts in the stability of input data distributions:

• Population Stability Index (PSI)
• KL Divergence, KS Test
• Statistical changes in numerical features (mean / var / quantile shift)
• Distribution drift in categorical features (frequency shift)

3. Prediction Drift

Monitor abnormal model output behavior:

• Changes in output distribution
• Binning stability (e.g., deciles shift)
• Prediction differences across groups/segments

4. Automated Reporting

Unified report generation capabilities:

• Automatically generate drift charts (matplotlib / plotly)
• One‑click HTML or PDF report generation
• Extensible storage or push to custom dashboards

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🔧 Installation

pip install momo-ml