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

Preparing data for AutoML regression

Before you can train any model with AutoML, you must have a properly cleansed dataset. This section will walk you through how to prepare data for any AutoML regression solution. You will begin by using your compute instance to access Jupyter notebook, a code editor that will let you code in Python. Following that, you will cleanse, transform, and register your data as an Azure dataset. This will give you a dataset that's ready for training in the next section.

Some of you may be new to Python or even to coding in general, but don't worry. While scripting an AutoML solution may seem much more difficult than using the GUI, in reality, it's a matter of making slight changes to boilerplate code.

Using the code found in this book's GitHub repository, you only have to alter it slightly to adapt it to your own custom solution using your own custom data. Furthermore, for this exercise, you've already completed most of the...