nannyml

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nannyml: post-deployment data science in python

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- - bug_report.md
  - feature_request.md
- - dev.yml
  - preview.yml
  - release.yml
- CODEOWNERS
- ISSUE_TEMPLATE.md
- stale.yml
- Profiling BC.ipynb
- Profiling MC.ipynb
- Profiling REG.ipynb
- - nbimport.py
  - nbtable.py
- - .gitkeep
  - deep_dive_data_chunks_minimum_chunk_size.svg
  - deep_dive_performance_estimation_calibration_curves.png
  - drift-guide-categorical.svg
  - drift-guide-continuous.svg
  - drift-guide-joyplot-continuous.svg
  - drift-guide-joyplot-distance_from_office.svg
  - drift-guide-stacked-categorical.svg
  - drift-guide-stacked-salary_range.svg
  - drift-guide-work_home_actual.svg
  - drift-guide-y_pred.svg
  - example_california_latitude_longitude_scatter.svg
  - example_california_performance.svg
  - example_california_performance_distribution.svg
  - example_california_performance_distribution_Latitude.svg
  - example_california_performance_distribution_Longitude.svg
  - example_california_performance_estimation_tmp.svg
  - example_green_taxi_all_udc.svg
  - example_green_taxi_dle.svg
  - example_green_taxi_dle_vs_realized.svg
  - example_green_taxi_feature_importance.svg
  - example_green_taxi_location_udc.svg
  - example_green_taxi_model_val.png
  - example_green_taxi_pca_error.svg
  - example_green_taxi_pickup_udc.svg
  - example_green_taxi_tip_amount_boxplot.svg
  - example_green_taxi_tip_amount_distribution.svg
  - guide-chunking_your_data-pe_plot.svg
  - guide-performance_estimation_tmp.svg
  - how-it-works-cat_hellinger.svg
  - how-it-works-cat_js.svg
  - how-it-works-chi2.svg
  - how-it-works-dle-data.svg
  - how-it-works-dle-regression-abs-errors-hist.svg
  - how-it-works-dle-regression-errors-hist.svg
  - how-it-works-dle-regression-PI.svg
  - how-it-works-dle-regression.svg
  - how-it-works-emd.svg
  - how-it-works-hellinger.svg
  - how-it-works-js.svg
  - how-it-works-ks.svg
  - how-it-works-linf.svg
  - how-it-works-ranking-abs-perf-features-compare.svg
  - how-it-works-ranking-abs-perf.svg
  - how-it-works-univariate-drift-detection-js-ks.svg
  - how-it-works-univariate-drift-detection-ks.svg
  - how-it-works-univariate-drift-detection-wasserstein-area.svg
  - how-it-works-univariate-drift-detection-wasserstein-pdf-cdf.svg
  - how_it_works_data_reconstruction_drift.svg
  - pca_reconstruction_error.svg
  - placeholder.svg
  - target_distribution_statistical.svg
  - tutorial-perf-est-guide-analysis-f1.svg
  - tutorial-perf-est-guide-analysis-roc_auc.svg
  - tutorial-perf-est-guide-analysis.svg
  - tutorial-perf-est-guide-hover-roc-auc.png
  - tutorial-perf-est-guide-with-ref-f1.svg
  - tutorial-perf-est-guide-with-ref-roc_auc.svg
  - tutorial-perf-est-guide-with-ref.svg
  - tutorial-perf-est-mc-guide-analysis-f1.svg
  - tutorial-perf-est-mc-guide-analysis-roc_auc.svg
  - tutorial-perf-est-mc-guide-hover-roc-auc.png
  - tutorial-perf-est-mc-guide-with-ref-f1.svg
  - tutorial-perf-est-mc-guide-with-ref-roc_auc.svg
  - tutorial-perf-est-regression-RMSE.svg
  - tutorial-perf-est-regression-RMSLE.svg
  - tutorial-perf-est-regression.svg
  - tutorial-perf-guide-Accuracy.svg
  - tutorial-perf-guide-F1.svg
  - tutorial-perf-guide-hover-ROC_AUC.png
  - tutorial-perf-guide-mc-F1.svg
  - tutorial-perf-guide-mc-hover-ROC_AUC.png
  - tutorial-perf-guide-mc-ROC_AUC.svg
  - tutorial-perf-guide-Precision.svg
  - tutorial-perf-guide-Recall.svg
  - tutorial-perf-guide-regression-mae.svg
  - tutorial-perf-guide-regression-mape.svg
  - tutorial-perf-guide-regression-mse.svg
  - tutorial-perf-guide-regression-msle.svg
  - tutorial-perf-guide-regression-rmse.svg
  - tutorial-perf-guide-regression-rmsle.svg
  - tutorial-perf-guide-ROC_AUC.svg
  - tutorial-perf-guide-Specificity.svg
  - usage_logging_how_it_works.png
- - layout.html
- - commands.rst
  - configuration_file.rst
  - running_the_cli.rst
- - quickstart_what_is_nannyml.rst
  - tutorials_detecting_data_drift.rst
  - tutorials_detecting_drift_univariate_walkthrough.rst
- - binary_car_loan.rst
  - california.rst
  - ma_employment.rst
  - multiclass.rst
  - regression.rst
  - titanic.rst
- - .gitignore
  - California-Housing.ipynb
  - Datasets - Census Employment MA.ipynb
  - Datasets - Multiclass.ipynb
  - Examples California Housing.ipynb
  - Examples Green Taxi.ipynb
  - How it Works - Chunking Data.ipynb
  - How it Works - DLE.ipynb
  - How It Works - Multivariate Drift.ipynb
  - How it Works - Ranking.ipynb
  - How it Works - Thresholds.ipynb
  - Quickstart.ipynb
  - Review Comparison Plots.ipynb
  - Tutorial - Adjusting plots.ipynb
  - Tutorial - Calculating Business Value - Binary Classification.ipynb
  - Tutorial - Calculating Business Value - Multiclass Classification.ipynb
  - Tutorial - Calculating Confusion Matrix - Binary Classification.ipynb
  - Tutorial - Calculating Confusion Matrix - Multiclass Classification.ipynb
  - Tutorial - Calculating Standard Metrics - Binary Classification.ipynb
  - Tutorial - Chunking.ipynb
  - Tutorial - Compare Estimated and Realized Performance.ipynb
  - Tutorial - Creating and Estimating a Custom Metric - Binary Classification.ipynb
  - Tutorial - Data Requirements.ipynb
  - Tutorial - Drift - Multivariate - Domain Classifier.ipynb
  - Tutorial - Drift - Multivariate.ipynb
  - Tutorial - Drift - Univariate.ipynb
  - Tutorial - Estimating Business Value - Binary Classification.ipynb
  - Tutorial - Estimating Business Value - Multiclass Classification.ipynb
  - Tutorial - Estimating Confusion Matrix - Binary Classification.ipynb
  - Tutorial - Estimating Confusion Matrix - Multiclass Classification.ipynb
  - Tutorial - Estimating Performance - Multiclass Classification.ipynb
  - Tutorial - Estimating Performance - Regression.ipynb
  - Tutorial - Estimating Standard Performance Metrics - Binary Classification.ipynb
  - Tutorial - Missing Values.ipynb
  - Tutorial - Ranking.ipynb
  - Tutorial - Realized Performance - Multiclass Classification.ipynb
  - Tutorial - Realized Performance - Regression.ipynb
  - Tutorial - Stats - Avg.ipynb
  - Tutorial - Stats - Count.ipynb
  - Tutorial - Stats - Median.ipynb
  - Tutorial - Stats - Std.ipynb
  - Tutorial - Stats - Sum.ipynb
  - Tutorial - Storing and Loading Calculators - Univariate.ipynb
  - Tutorial - Thresholds.ipynb
  - Tutorial - Unseen Values.ipynb
  - Tutorial - Working with results.ipynb
- - california_housing.rst
  - green_taxi.rst
- - business_value.rst
  - chunking_data.rst
  - estimation_of_standard_error.rst
  - multivariate_drift.rst
  - performance_estimation.rst
  - ranking.rst
  - thresholds.rst
  - univariate_drift_comparison.rst
  - univariate_drift_detection.rst
- - adjusting_plots.rst
  - chunking.rst
  - compare_estimated_and_realized_performance.rst
  - data_quality.rst
  - data_requirements.rst
  - detecting_data_drift.rst
  - performance_calculation.rst
  - performance_estimation.rst
  - ranking.rst
  - storing_and_loading_calculators.rst
  - summary_stats.rst
  - thresholds.rst
  - working_with_results.rst
- cli.rst
- conf.py
- contributing.rst
- datasets.rst
- examples.rst
- glossary.rst
- how_it_works.rst
- index.rst
- installing_nannyml.rst
- landing_page.rst
- Makefile
- quick.rst
- requirements.txt
- run_notebooks.py
- tutorials.rst
- usage_logging.rst
- utils.py
- cbpe_v3.gif
- cbpe_v4.gif
- estimate-performance-regression.gif
- estimate-post-deployment-model-performance.gif
- github.png
- slack.png
- thumbnail-4.png
- - __init__.py
  - cli.py
  - run.py
- - __init__.py
- - __init__.py
  - datasets.py
- - __init__.py
- - __init__.py
  - ranker.py
- - __init__.py
  - base.py
  - file_reader.py
  - file_writer.py
  - pickle_file_writer.py
  - raw_files_writer.py
- - __init__.py
  - calculator.py
  - result.py
- - __init__.py
- - __init__.py
  - colors.py
  - util.py
- - __init__.py
  - binary_classification.py
  - multiclass_classification.py
  - regression.py
  - summary_stats.py
- - __init__.py
- __init__.py
- _typing.py
- analytics.py
- base.py
- calibration.py
- chunk.py
- config.py
- exceptions.py
- runner.py
- thresholds.py
- usage_logging.py
- - test_missing.py
  - test_range.py
  - test_unseen.py
- - test_drift.py
  - test_multiv_dc.py
  - test_multiv_pca.py
  - test_output_drift.py
  - test_target_distribution.py
  - test_univariate_drift_methods.py
- - test_writers.py
- - test_performance_calculator.py
- - test_base.py
- - comparison_plots.py
- - test_binary_classification_sampling_error.py
  - test_multiclass_classification_sampling_error.py
  - test_regression_sampling_error.py
- - test_avg.py
  - test_count.py
  - test_median.py
  - test_std.py
  - test_sum.py
- __init__.py
- conftest.py
- test_base.py
- test_calibration.py
- test_chunk.py
- test_datasets.py
- test_ranking.py
- test_runner.py
- test_thresholds.py
- test_typing.py
.bumpversion.cfg
.dockerignore
.editorconfig
.gitignore
.pre-commit-config.yaml
.readthedocs.yaml
CHANGELOG.md
CONTRIBUTING.rst
Dockerfile
LICENSE
makefile
poetry.lock
pyproject.toml
README.md
setup.cfg
setup.py

# Installation Guide

1. Get the code

git clone https://github.com/NannyML/nannyml

Downloads the entire project code from GitHub to your computer.

cd nannyml

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install nannyml

Installs the package published on PyPI directly — no need to clone the source.

pip install nannyml[db]

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Docker

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Docker Desktop Needed to build and run containers. Install it and keep it running in the background.

docker -v /local/config/dir/:/config/ run nannyml/nannyml nml run

Type this command into your terminal and run it.

✅ Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.

Pulled directly from this repo's README.

4. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install nannyml

Installs the package published on PyPI directly — no need to clone the source.

python -m pip install git+https://github.com/NannyML/nannyml

Installs the Python libraries listed in requirements.txt (or similar).

pip install nannyml[db]

Installs the package published on PyPI directly — no need to clone the source.

poetry install nannyml --all-extras

Installs the libraries listed in pyproject.toml.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

5. Make

Medium

Prerequisites

Git Needed to download the project code from GitHub.
Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).

make

Compiles the code based on the generated build configuration to produce an executable.

✅ If it finishes without errors, it worked. Try running the generated executable directly.

nannyml

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# Installation Guide

2. Official Install Script

3. Docker

4. Python

5. Make

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nannyml

File Explorer

# Installation Guide

2. Official Install Script

3. Docker

4. Python

5. Make

Today's picks

Was this content helpful?

Recent Feedback

Download README