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 forecasting model

Training an AutoML forecasting is most similar to training an AutoML regression model. Like regression and unlike classification, you are trying to predict a number. Unlike regression, this number is always in the future based on patterns found in the past. Also, unlike regression, you can predict a whole series of numbers into the future. For example, you can choose to predict one month out into the future or you can choose to predict 6, 12, 18, or even 24 months out.

Important tip

The further out you try to predict, the less accurate your forecasting model will be.

Follow the same steps you have seen in Chapter 4, Building an AutoML Regression Solution, and Chapter 5, Building an AutoML Classification Solution. First, begin by setting a name for your experiment. Then, set your target column and your AutoML configurations.

For forecasting, there is an additional step: setting your forecasting parameters. This is where you will set things...