NannyML
Monitors deployed ML models, estimating performance without ground-truth labels and flagging data and concept drift.
How the 6.2 was reached
- Quality of output6/10
Documented CBPE and DLE algorithms; 2,156 stars but last push July 2025, modest adoption. github.com
- Ease of use7/10
Open-source Python library with cloud docs and 30-day trials. github.com
- Pricing value6/10
Free open source, but cloud plans start at $399/month for 2 models, which is steep. nannyml.com
- API and integration quality6/10
Cloud SDK exists, with API access only in Scale and Enterprise plans. docs.nannyml.com
- Solves the problem it claims to solve6/10
Delivers performance estimation without target data for tabular models; adoption is modest with 2,156 stars. github.com
6.2/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
- NannyML OSS Python library
- Performance estimation and drift detection
- 2 models
- 10M predictions
- Email support
- 30-day free trial
- 6 models
- Private Slack
- API access
- 30-day free trial
- Unlimited models
- Unlimited predictions
- 24/7 support
- 30-day free trial
Key Features
- Confidence-based performance estimation (CBPE)
- Direct loss estimation (DLE)
- Multivariate drift detection using PCA-based data reconstruction
- Model-agnostic, covering tabular classification and regression
- Alerts that link drift to performance changes
Pros & Cons
Pros
- Estimates performance when labels are delayed or missing
- Free open-source library with interactive visualizations
- Documented algorithms
- 30-day trials on every paid cloud plan
Cons
- Cloud starts at $399/month for only 2 models
- API access is limited to Scale and Enterprise
- Tabular data only
- Modest adoption, with the last GitHub push in July 2025
NannyML fits data science teams with tabular models and slow label feedback who can start on the free library. Teams with many models will find cloud pricing steep, and anyone working with text or images should look elsewhere. A fair choice, not a standout.
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