Your Model Isn't Done Until Someone Else Can Call It

September 13, 2026 | Source: Towards Data Science

Tags: FastAPI, Docker, scikit-learn, MLOps, model deployment, Python, churn prediction

A hands-on tutorial covers containerizing a scikit-learn churn-prediction model with Docker and deploying it as a public FastAPI endpoint — including three real deployment failures the author hit along the way.

Details

This is Part B of a series on building production ML APIs. The author previously trained a churn prediction model and wrapped it in a FastAPI /predict endpoint that worked locally; this installment covers what it takes to make that API reachable by anyone on the internet. The setup: a scikit-learn pipeline (scaler + classifier) exposed via a single /predict endpoint that returns a probability, a prediction, and a risk level. Core steps covered include generating an exact requirements.txt with pip freeze — capturing fastapi==0.141.1, uvicorn[standard]==0.40.0, and scikit-learn — writing a Dockerfile that packages the app as a self-contained image, and deploying to a real server. Three failure modes encountered during the process are described (the feed excerpt cuts off before full detail). The piece targets ML engineers who have trained models but have not yet learned deployment fundamentals. The Docker approach advocated — pinning exact package versions and bundling everything into an image — is sound production practice and a common first step toward reproducible ML deployments.