This project demonstrates the complete machine learning lifecycle using MLOps practices, from model development and training to deployment, monitoring, and continuous integration.
- data/: Contains raw and processed datasets.
- src/: Contains the source code for data processing, model training, and evaluation.
- model/: Stores trained models and associated artifacts.
- config/: Configuration files such as YAML files for hyperparameters and settings.
- Clone the repository:
git clone -b main <repository_url> - Install dependencies:
pip install -e .
- Run the training script:
python pipeline.py