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- The ML workflow typically begins with data acquisition (collecting and assembling the raw dataset), followed by exploratory data analysis (EDA), feature engineering, model training, validation, testing, deployment, and finally production monitoring. Hyperparameter tuning happens after a baseline model exists; deployment and drift monitoring are late-stage activities.