The Machine Learning Project Lifecycle - TODO

The Machine Learning Project Lifecycle describes the structured process of taking a machine learning based solution from concept to production and beyond. It begins with clearly defining the problem and success criteria, followed by collecting and preparing data, developing and training models, and validating their performance. The lifecycle continues with deploying models into production, monitoring their behavior in real-world conditions, and continuously improving them through updates and retraining. Understanding this lifecycle helps teams build reliable, scalable, and maintainable machine learning systems that deliver long-term business value.