The document outlines the typical steps in a machine learning workflow including data ingestion, cleansing and transformation, model training and building, testing and validation, deployment, monitoring and feedback loops. It shows the iterative process starting with data preparation, then model training, testing and deployment, and continuous improvement through monitoring and further analysis.