1. Machine Learning Is a Software Discipline

ML is not just about training models — it’s an engineering process. The book emphasizes that building a successful ML system involves understanding the problem, choosing relevant metrics (like RMSE, AUC), splitting datasets fairly, and implementing reproducible code. You must establish data pipelines, testing routines, and model versioning from day one. Like in traditional software, modularity, documentation, and validation are essential. ML without engineering rigor is just experimentation. Géron teaches that true ML mastery lies in design thinking, process management, and code integrity.