A real ML project follows a repeating cycle — not a straight line from data to deployment.

The ML Lifecycle

1. Define the problem: What are we predicting? What metric matters? What's the baseline?
2. Collect and explore data: EDA, quality assessment, labeling
3. Preprocess and engineer features: Cleaning, transforming, feature creation
4. Train and evaluate models: Baseline → iterate → compare
5. Tune hyperparameters: Grid search, random search, Bayesian optimization
6. Deploy the model: API, batch pipeline, or embedded
7. Monitor in production: Data drift, performance degradation, retraining

Reference:

MLOps principles

https://ml-ops.org/