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 now have a firm understanding of batch and real-time inferencing, and when to use which type of scoring solution. This is important, as even seasoned data scientists occasionally make mistakes when designing end-to-end ML solutions.

Furthermore, most ML courses focus on training models instead of deploying them, but to be an effective data scientist, you must be proficient at both. In the upcoming chapters, you will learn how to code each of these inferencing methods in AMLS.

In Chapter 9, Implementing a Batch Scoring Solution, you will learn step by step how to use the ML models you've already built in batch scoring scenarios. You will create ML pipelines in AMLS and learn how to schedule them to run on a timer. This will allow you to easily productionalize your ML models and become a valuable asset to your company or organization.