NannyML

Monitor your production machine learning models for silent failures, performance degradation, and data drift.

NannyML provides a Python library and cloud dashboard to track model health without needing access to ground truth.

Best forMonitoring production ML models

DifferentiatorEstimates performance without ground truth labels

ProofActive open-source project with paid cloud offering

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8.8 Zekai

Essential MLOps Monitoring

AI for Software Development

Ease of Use

8.1

Accuracy

9.2

Value

9.3

Time Saving

8.6

Open SourcePerformance EstimationData DriftConcept DriftPython Library

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⚡ Quick answer

For AI software development, one of the best tools for post-deployment monitoring is NannyML. Its key strength is its open-source library that can estimate model performance (like AUC) and detect data drift without needing access to ground truth labels, preventing silent failures. This makes it highly valuable for teams that need to maintain model reliability in real-time.

CategoryMLOps

Best ForMonitoring production ML models

Price FromFree (open-source)

FreeYes

DifferentiatorEstimates performance without ground truth labels

ProofActive open-source project with paid cloud offering

Rating4.4

📖 About NannyML

NannyML is an open-source library for monitoring machine learning models in production. It helps data scientists and ML engineers detect silent model failures by estimating post-deployment model performance, tracking data drift, and identifying concept drift without waiting for ground truth labels.

How It Works

Your workflow, automated

Install and Prepare Data

Install the open-source library (`pip install nannyml`) and prepare your reference and analysis dataframes containing model inputs, outputs, and timestamps.

Run Calculators

Instantiate and run NannyML calculators for performance estimation (CBPE), data drift, or concept drift on your data.

Visualize or Alert

Use the built-in plotting functions to generate visual reports or integrate the result data with alerting systems like PagerDuty or Slack.

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Real Impact

Before & After

❌ Before

Models in production fail silently with no clear indication of why or when.

Weeks to detect issues

✅ After

Real-time visibility into model performance and data drift, with alerts on potential issues.

Hours to detect issues

Social Proof

Trusted by professionals

Ease of Use

8.1

Accuracy

9.2

Value

9.3

Time Saving

8.6

"NannyML has become our standard for production monitoring. The ability to estimate performance with CBPE before we get labels is a game-changer for our fraud models."

David C., Lead ML Engineer · June 2026

"The open-source library is incredibly powerful. We've integrated it into our Airflow DAGs to generate a monitoring report for every model, every day. Indispensable."

Maria S., Data Scientist · May 2026

"It's a fantastic tool, but there's a definite learning curve. You need to be comfortable in Python and understand the MLOps concepts. It's not a plug-and-play GUI solution."

Ben T., AI Developer · April 2026

"We evaluated several tools, and NannyML's focus on open-source flexibility won us over. We can customize everything and we aren't locked into a proprietary ecosystem."

Fatima A., Head of MLOps · March 2026

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Connects With

Works with your existing stack

Python Airflow Kubeflow Prefect AWS S3 Snowflake Pandas scikit-learn XGBoost LightGBM PyTorch TensorFlow

Setup complexity: Advanced

Who It's For

Why Software Development choose this tool

🎯

Built for

This tool is best for ML engineering teams needing a robust, open-source solution to monitor and prevent performance degradation of live machine learning models.

In-Depth Overview

For software developers working with machine learning, the post-deployment phase is critical and often opaque. Models can fail silently due to data drift or concept drift, leading to poor business outcomes. NannyML directly addresses this by providing tools to estimate model performance in real-time, even without immediate access to target data. Its key capability is its performance estimation algorithms (like Confidence-based Performance Monitoring and Direct Loss Estimation), which calculate likely performance metrics (AUC, F1 score, etc.) based on the input data and model outputs alone. This allows you to set up alerts and dashboards to catch issues early. Because it's an open-source Python library, it integrates directly into your existing MLOps pipelines (using tools like Airflow or Kubeflow) and works with any model, regardless of the framework (Scikit-learn, TensorFlow, PyTorch). The optional cloud dashboard provides a visual interface for monitoring and collaboration, turning raw monitoring data into actionable insights for the entire development team.

Key Use Cases

🤖

Prevent Silent Model Failure

ML Engineer

Set up automated checks to estimate your model's AUC score in production daily, getting alerts when performance is predicted to drop below a critical threshold.

Early issue detection

📊

Diagnose Performance Degradation

Data Scientist

Use NannyML's drift detection to pinpoint which specific features are causing a drop in model accuracy, enabling faster debugging and retraining.

Reduced debugging time

📈

Maintain Business Impact

Product Manager

Monitor a model's real-world business value by tracking its estimated performance against key metrics, ensuring it continues to deliver results.

Sustained ROI

✓ Pros

Open-source and framework-agnostic

Estimates performance without waiting for target labels

Comprehensive drift detection capabilities

Strong documentation and community support

Integrates into existing data pipelines

· Cons

Requires coding and MLOps knowledge to implement effectively

Cloud dashboard is a paid, separate product

Steeper learning curve than fully managed, GUI-based platforms

⚡ Editorial Verdict

NannyML is a powerful open-source tool for a crucial MLOps task: post-deployment monitoring. Its ability to estimate performance without ground truth is a significant advantage for preventing silent model failure. The main trade-off is that while the core library is flexible, setting up a complete, automated monitoring system requires engineering effort and integration work.

See all Software Development AI tools → Browse the AI Directory →

Questions & Answers

Frequently asked questions

How does NannyML estimate performance without ground truth?

NannyML uses algorithms like Confidence-based Performance Estimation (CBPE), which analyzes the model's confidence scores on output predictions to estimate performance metrics like AUC and F1. This allows you to monitor performance without waiting for actual outcomes.

Is NannyML free to use?

Yes, the core NannyML library is open-source and free to use. They also offer a paid cloud platform that provides a user interface, dashboards, and managed services for monitoring.

What's the difference between data drift and concept drift?

Data drift is when the statistical properties of your input data change (e.g., a new category appears). Concept drift is when the relationship between input data and the target variable changes. NannyML helps detect both.

Does NannyML work with my existing MLOps stack?

Yes, as a Python library, NannyML is designed to be integrated into any MLOps stack. You can run it within orchestration tools like Airflow, Kubeflow, or Prefect and connect it to data sources like S3, Snowflake, or standard databases.

What models can I monitor with NannyML?

NannyML is model-agnostic. It works with any classification or regression model from any framework (like Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch) as long as you can provide the required inputs (model predictions, probabilities, and feature data).

Where is the NannyML company headquartered?

NannyML is a Belgium-based company. As it serves a global user base, its operational focus is not limited by its headquarters location.

Last reviewed: June 2026 — Assessed core library features, documentation, and cloud offering.

Plans & Pricing

Start today

Free

Get Started Free

Pro

Pricing on request

The paid NannyML Cloud platform requires a demo. Pricing is tailored to usage and team size.

Start Pro Plan

Enterprise

Pricing on request

Tailored for teams and large organisations.

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Prices and features are updated regularly but can change at any time — always confirm on the official website. Some links on this page are affiliate links.

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NannyML Alternatives (2026)

The best alternatives to NannyML — every tool below is tested and scored by Zekai.

Compare	NannyML	Hugging Face	SuperAnnotate
Zekai Score	8.8/10	9.4/10	9.3/10
Rating	4.4/5	4.7/5	4.7/5
Pricing	Pricing on request	$9/mo	Pricing on request
Free tier	Yes	Yes	No
Best for	This tool is best for ML engineering teams needing…	Best for developers needing to rapidly prototype a…	AI development teams that require custom, high-qua…

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Disclaimer

Zekai is an independent AI tools directory. We are not affiliated with, endorsed by, or officially connected to NannyML unless clearly stated. All product names, logos, and brands are the property of their respective owners and are used for identification purposes only. The information on this page — including pricing, features, and availability — is general information, may have changed since our last review, and is not professional advice. Zekai Scores and verdicts are our editorial opinion. Some outbound links are affiliate links that may earn us a commission at no extra cost to you. Spotted outdated or incorrect information? Request a correction →

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Your workflow, automated

Before & After

Trusted by professionals

Works with your existing stack