NannyML/nannyml

Health v2 · maintenance only

Adoption not part of the score

2155 stars · 199 forks observed · 2026-09-22

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NannyML is an open-source Python library for post-deployment machine learning monitoring. It estimates model performance without access to ground-truth labels, detects univariate and multivariate data drift, and links drift alerts to changes in model performance.

Use cases

• estimate ML model performance without ground truth labels
• detect data drift in production ML models
• monitor classification and regression model degradation
• link data drift alerts to model performance changes
• detect multivariate drift in feature distributions
• visualize model performance over time in Jupyter notebooks

When to choose

• you need to monitor deployed ML models when ground truth is delayed or unavailable
• you want a model-agnostic, Python-native monitoring library for tabular data
• you want to avoid alert fatigue from drift alerts that don't affect performance
• you prefer an open-source alternative to commercial ML monitoring platforms

When to avoid

• you need monitoring for image, text, or video models rather than tabular data
• you want a fully managed hosted monitoring service without self-managed infrastructure
• you need real-time streaming monitoring out of the box rather than batch analysis

Facets

library · maturity active

monitoring machine-learning data-visualization analytics machine-learning data-science monitoring python cross-platform ml-monitoring data-drift performance-estimation model-monitoring post-deployment mlops concept-drift

Member repositories

For agents

markdown · JSON · MCP: product_card(name="NannyML/nannyml")