The machine learning lifecycle from data collection to operations

Remember the machine learning lifecycle phases and the work done in each.
・Data collection: gather the data to be used for training.
・Data preparation: get the data into shape through cleansing (imputing missing values, handling outliers, standardizing formats) and feature engineering (creating useful input variables).
・Model training: train the model with an algorithm and tune the hyperparameters.
・Evaluation: check model performance with metrics such as accuracy.
・Deployment: deploy the model to the production environment.
・Monitoring (operations): monitor the model's behavior and accuracy in production and serve inference results via an API.
In this question, 'data cleansing and feature engineering' belong to the data preparation phase. Production monitoring and inference API serving belong to the post-deployment operations phase, and hyperparameter tuning belongs to the model training phase.