From scikit-learn to Production, Deploying ML Models That Actually Work

There is a gap between training a model in a Jupyter Notebook and running it in production. Most tutorials stop at model.score() and call it done. This article covers the full pipeline: data preprocessing, model selection, evaluation, serialization, and serving a scikit-learn model behind a FastAPI endpoint. The Problem We needed a transaction risk scoring system for a crypto payment gateway. The model receives transaction features and returns a fraud probability between 0 and 1. Requirements: Latency under 50ms per prediction Handle 1,000 requests per second Update the model weekly without downtime Explainable predictions (regulators want to know why a transaction was flagged) scikit-learn turned out to be the right tool. Not TensorFlow, not PyTorch. For tabular data with fewer than 100 features, gradient boosted trees in scikit-learn are hard to beat. Data Preprocessing Pipeline Raw transaction data is messy. Missing values, mixed types, different scales. scikit-learn pipelines handl