Instructions to use MathisKhan/mlops-versioning-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use MathisKhan/mlops-versioning-demo with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("MathisKhan/mlops-versioning-demo", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
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Download README.md from MathisKhan/mlops-versioning-demo: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
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https://huggingface.co/MathisKhan/mlops-versioning-demo/resolve/main/README.md
- Command line
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hf download hf://MathisKhan/mlops-versioning-demo/README.md
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curl -L -o README.md https://huggingface.co/MathisKhan/mlops-versioning-demo/resolve/main/README.md
190 Bytes
metadata
tags:
- sklearn
- mlops-demo
Modele de versioning demo - v2
LogisticRegression entrainee sur le dataset Iris.
- Accuracy (test) : 1.0000
- Hyperparametres : C=0.1, max_iter=500