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


You have now created and tested real-time scoring solutions using an AutoML trained model. Deploying first on ACI and then on AKS, you understand the full end-to-end process of creating a real-time scoring endpoint.

Furthermore, you understand how data must be shaped and formatted in order to generate predictions using these endpoints, which can be incorporated into any piece of code using a wide variety of computer languages to create powerful, innovative solutions.

In the next chapter, Chapter 12, Realizing Business Value with AutoML, the final chapter of the book, you will learn how to present AutoML solutions in a way that will gain the trust of your non-technical business partners. Their trust and acceptance, after all, is the foundation to unlocking the power and value of ML and artificial intelligence in your organization.