23.1 Introduction

Although you may have learned how to store ML models for later use in a predictive environment, it may help to also see how those models can be deployed in a cloud infrastructure as a POST API. This makes the prediction particularly easy to consume by a software development team who may need to use your predictions in an app or website.

This chapter will walk you though creating a student account in the Microsoft Azure Machine Learning platform, generating python in a Jupyter Notebook, saving a trained model, and deploying that model. Azure (simlar to Amazon AWS and Google Cloud) allows you to connect your code to a live data warehouse and rerun your code on a set schedule to complete a true ML pipeline.

Let's begin in the next section by creating your Azure for Students account.