End-to-End Machine Learning: What Really Happens Between Data and Deployment

A practical guide published on DEV Community outlines the full lifecycle of a machine learning project, from problem definition to deployment and monitoring. The guide challenges the common beginner assumption that training a model and checking its accuracy marks the end of a project. It walks through key stages including data collection, preprocessing, exploratory analysis, model training, evaluation, and deployment, using a customer churn prediction scenario as a running example. The article highlights that raw data often requires significant cleaning — handling missing values, encoding categorical variables, and scaling features — before it can be used by any algorithm. It emphasizes that starting with a clearly defined problem, rather than jumping straight to algorithm selection, is critical to building a model that genuinely solves a real-world need.