How to Prepare Scikit-Learn Models for Production

Data Scientists spend large amounts of effort gathering business requirements, performing exploratory data analysis, data pre-processing, feature engineering, hyperparameter tuning and model evaluation only to have their models stuck in local notebook environments. In order to unlock the full value of the trained models, the models have to be made available to downstream applications. In this article, we walk through the steps to serve scikit-learn machine learning models to downstream applications using Docker and FastAPI. In essence, we will be training a model, wrap the model into an API and containerize the application. Docker is an open platform for developing, shipping, and running applications.