What is the Machine Learning workflow or pipeline?

The ML workflow is a systematic process from problem definition to model deployment, ensuring structured and reproducible machine learning projects. • **Iterative Process:** Continuous improvement through feedback loops • **Documentation:** Track experiments, decisions, and model versions • **Validation:** Test and validate at each stage before proceeding • **Monitoring:** Track model performance in production **Example:** Building a customer churn predictor involves defining churn metrics, collecting customer data, cleaning and feature engineering, training multiple algorithms, evaluating with cross-validation, and deploying the best model for real-time predictions with ongoing monitoring.