Deploying Models in Production with Scikit-Learn and PySpark

Abstract

In this final chapter of the book, we explore the practical aspects of deploying machine learning models using Scikit-Learn and PySpark. Model deployment is the process of making a machine learning model available for use in a production environment where it can make predictions or perform tasks based on real-world data. It involves taking a trained machine learning model and integrating it into a system or application so that it can provide predictions to end users or other systems.