Serve your first model with Scikit-Learn + Flask + Docker

One of the first steps in achieving this is to create a process to serve machine learning models to the organization. This is usually done by creating an application to run the prediction model and return the prediction, in the example in this post we are going to use a handy stack to create and serve models. We will be using Python as the base programming language, the Scikit-Learn package for building the model pipeline: preprocessing the data, training the model and saving the model into a file, the Flask package to develop a web application for the interaction between the client and the prediction model and finally Docker for containerizing the application to prepare it for deployment. In this example we are going to work with the dataset: Breast Cancer Wisconsin (Diagnostic) [1], a widely used dataset for testing machine learning models. In this dataset features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass and it was first introduced in K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23–34].