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

Registering your trained regression model

AutoML lets you easily register your trained models for future use. In Chapter 9, Implementing a Batch Scoring Solution, and Chapter 11, Implementing a Real-Time Scoring Solution, you will create batch execution inference pipelines and real-time scoring endpoints that will use your models. When registering your model, you can add tags and descriptions for easier tracking.

One especially useful feature is the ability to register models based on metrics other than the one you used to score your model. Thus, even though you trained a model using normalized RMSE, you can also register the model that had the best R2 score, even if that model is different.

In this section, you will write a simple description of your model, tag it, and give it a name. After that, you will register the model to your AMLS workspace. It also contains code that will let you register different models based on other metrics. Let's get started:

  1. First...