The steps in a machine learning pipeline involve data collection, preprocessing (cleaning, feature engineering), model selection, training, evaluation, tuning, deployment, and ongoing monitoring. It starts with gathering and preparing data, progressing through model building, assessment, and deployment into a functional, monitored production environment.