My Model Worked Perfectly. Then I Tried to Make It Useful.

A machine learning model that works in a notebook is not truly useful until it is deployed as a service for others to use. The process of making a model useful involves turning a trained churn classifier into a FastAPI service that other software can call. Key steps include defining the API's input and output boundary, sharing the exact same preprocessing code between training and inference, and structuring the project to separate the live service from the training process. Practical optimizations, such as loading the model into memory once at startup, are crucial for creating a performant and usable API.