Machine Learning for Time Series Forecasting with Python

CHAPTER 6Model Deployment for Time Series Forecasting

Throughout the book, I introduced a few real-world data science scenarios that I used to showcase some of the key time series concepts, steps, and codes. In this final chapter, I will walk you through the process of building and deploying some of the time series forecasting solutions by employing some of these use cases and data sets.

The purpose of this chapter is to provide a complete overview of tools to build and deploy your own time series forecasting solutions by discussing the following topics:

• Experimental Set Up and Introduction to Azure Machine Learning SDK for Python – In this section, I will introduce Azure Machine Learning SDK for Python to build and run machine learning workflows. You will get an overview of some of the most important classes in the SDK and how you can use them to build, train, and deploy a machine learning model on Azure.

Specifically, in this section you will discover the following concepts and assets:

• • Workspace, which is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models.
  • Experiment, which is another foundational cloud resource that represents a collection of trials (individual model runs).
  • Run, which represents a single trial of an experiment.
  • Model, which is used for working with cloud representations of the machine learning model.
  • ComputeTarget, RunConfiguration, ScriptRunConfig, which are ab-stract parent classes for creating ...