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
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