NannyML
Python library for monitoring model performance drift post-deployment.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is NannyML?
NannyML is a Python library that helps in capturing the impact of data drift on model performance after deployment. It allows users to estimate model performance without needing access to target labels, making it invaluable for continuous monitoring and maintenance of machine learning models.
Key differentiator
“NannyML stands out by offering a robust solution for estimating post-deployment model performance without the need for target labels, making it ideal for continuous monitoring in production environments.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
Primary development and maintenance focus on Python, limited official support for other languages
Processing time increases exponentially with dataset size, impacting real-time monitoring capabilities
Core concepts are not well-explained, limited practical use cases provided in the documentation
Fit analysis
Who is it for?
✓ Best for
Teams needing continuous monitoring of ML models without access to ground truth labels.
Projects where real-time performance estimation is critical for maintaining model reliability.
✕ Not a fit for
Scenarios requiring real-time data drift detection and immediate corrective actions.
Environments with limited computational resources, as NannyML requires significant processing power.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Next step
Get Started with NannyML
Step-by-step setup guide with code examples and common gotchas.