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


In this chapter, you have learned about all the prerequisites that are necessary for creating AutoML solutions in Azure. You created an AMLS workspace and accessed AML studio before creating the necessary compute to run and write your AutoML jobs. You then loaded data into a datastore and registered it as a dataset to make it available for your AutoML runs.

Importantly, you should now understand the four steps of the AutoML process: a data guardrails check, intelligent feature engineering, data transformation, and iterative ML model building. Everything you have done in this chapter will enable you to create a ML model in record time.

You are now ready for Chapter 3, Training Your First AutoML Model, where you will build your first AutoML model through a GUI. This chapter will cover a range of topics, from examining data to scoring models and explaining results. By the end of that chapter, you will not only be able to train models with AutoML, but you will also be able...