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


You have added to your repertoire by successfully training a classification model using the AML Python SDK. You have loaded in data, heavily transformed it using pandas and Numpy, and built a toy AutoML model. You then registered that model to your AMLS workspace.

You can now start building classification models with your own data. You can easily solve both binary and multiclass classification problems, and you can present results to the business in a way they understand with confusion matrices. Many of the most common business problems, such as customer churn, are classification problems, and with the knowledge you learned in this chapter, you can solve those problems and earn trust and respect in your organization.

The next chapter, Chapter 6, Building an AutoML Forecasting Solution, will be vastly different from the previous two chapters. Forecasting problems have many more settings to use and understand compared to classification and regression problems, and they...