Deploying a Scalable ML Pipeline in Production
Deploy a machine learning model on Heroku. Use Git and DVC to track code, data and model while developing a simple classification model on the Census Income Data Set. After creating the model, the finalize the model for production by checking its performance on slices and writing a model card encapsulating key knowledge about the model. Implement a Continuous Integration and Continuous Deployment framework and ensure pipeline passes a series of unit tests before deployment. Lastly, an API will be written using FastAPI and tested locally. After successful deployment, the API will be tested live using the requests module.
Execution
Model training Model training and test can be done by python main.py --choice train_model
Model score Check score on latest dvs saved model can be done by python main.py --choice get_score
Run entire pipeline To run the entire pipeline in sequence, use python main.py --choice all
Test API If testing FastAPi serving on local is needed, execute uvicorn app_server:app --reload
Check Heroku deployed API Check Heroku deployed API using python heroku_api_test.py
CI/CD
Every new commit triggers a test pipeline . This triggers a pull from DVC and exectute Pytest and Flake8 with Github actions.