Book Image

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers
Book Image

Automated Machine Learning with Microsoft Azure

By: Dennis Michael Sawyers

Overview of this book

Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.
Table of Contents (17 chapters)
Section 1: AutoML Explained – Why, What, and How
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions


With this chapter, you have successfully constructed a regression model using the AzureML Python SDK. Regardless of whether you're a Python novice or expert, you have loaded data, transformed it extensively using pandas, and built a useful machine learning model with AutoML. You then registered your model to an AMLS workspace. You will use that same model in future chapters to create inference pipelines and real-time scoring endpoints using REST APIs.

By working through all the exercises in this chapter, you have obtained a level of mastery over Azure AutoML regression solutions. You can now take any set of data that's useful in predicting a number and use it to create a high-performing machine learning model. Furthermore, you can code all of this in Python and, if the model fails to perform, you know lots of little ways to improve performance, or, if worst comes to worst, change your regression problem to a classification problem.

In Chapter 5, Building an...