Instructions to use VIMALRAJMVR/CI-CD_MLOps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use VIMALRAJMVR/CI-CD_MLOps with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("VIMALRAJMVR/CI-CD_MLOps", "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 VIMALRAJMVR/CI-CD_MLOps: direct link, hf CLI and curl.
- Browser
- Download file 240 Bytes
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https://huggingface.co/VIMALRAJMVR/CI-CD_MLOps/resolve/main/README.md
- Command line
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hf download hf://VIMALRAJMVR/CI-CD_MLOps/README.md
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curl -L -o README.md https://huggingface.co/VIMALRAJMVR/CI-CD_MLOps/resolve/main/README.md
240 Bytes
metadata
tags:
- sklearn
- random-forest
- mlops-pipeline
Model
Trained automatically via GitHub Actions CI/CD. Data and pipeline versioned with DVC.
Metrics
- R2_Score: 0.6793
- MSE: 0.4202
- MAE: 0.4608
- RMSE: 0.6483