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Basic overview
NannyML helps you monitor your ML models in production by:
- estimating performance in absence of ground truth
- calculating realized performance metrics
- detecting data drift on model inputs, model outputs and targets
Installing the latest stable release
pip install nannyml
Installing the latest development version
python -m pip install git+https://github.com/NannyML/nannyml
Getting started
import nannyml as nml
import pandas as pd
# Load some data
reference_data, analysis_data, _ = nml.load_synthetic_sample()
data = pd.concat([reference_data, analysis_data])
metadata = nml.extract_metadata(reference_data)
# Estimate performance
estimator = nml.CBPE(metadata).fit(reference_data)
estimates = estimator.estimate(data)
estimates.plot(kind='performance').show()
Examples
- Performance estimation
- Realized performance calculation
- Univariate model input drift detection
- Multivariate model input drift detection
- Model output drift detection
- Model target distribution
Development setup
- Read the docs on how to contribute
Release files for nannyml 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nannyml-0.3.1.tar.gz | 5.7 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nannyml-0.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 11.5 MB
Release files / nannyml-0.3.1.tar.gz
| Download URL | nannyml-0.3.1.tar.gz |
|---|---|
| Size | 5.7 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.9.12
|
Release files / nannyml-0.3.1-py3-none-any.whl
| Download URL | nannyml-0.3.1-py3-none-any.whl |
|---|---|
| Size | 5.8 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.9.12
|
