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

Training an AutoML multiclass model

Multiclass classification involves predicting three or more classes instead of the standard binary classification. Using custom machine learning, training multiclass models is often a messy, complicated affair where you have to carefully consider the number of classes you are trying to predict, how unbalanced those classes are relative to each other, whether you should combine classes together, and how you should present your results. Luckily, AutoML takes care of all these considerations for you and makes training a multiclass model as simple as training a binary classification model.

In this section, you load in data using the publicly available Iris dataset. You will then set your AutoML classifications for multiclass classification, train and register a model, and examine your results. You will notice that much of the code is identical to the last section. By understanding the differences between binary and multiclass classification in AutoML...