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

Understanding how AutoML works on Azure

Before running your first AutoML experiment, it's important to understand how AutoML works on Azure. AutoML is more than just machine learning, after all. It's also about data transformation and manipulation.

As shown in the following diagram, you can divide the stages of AutoML into roughly five parts: Data Guardrails Check, Intelligent Feature Engineering, Iterative Data Transformation, Iterative ML Model Building, and Model Ensembling. Only at the end of this process does AutoML produce a definitive best model:

Figure 2.17 – The Azure AutoML process

Let's take a closer look at each step in this process.

Ensuring data quality with data guardrails

Data guardrails check to make sure that your data is in the correct format for AutoML, and if it is not, it will alter the data accordingly. There are currently six main checks that are performed on your data. Two of the checks – one to...