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

Creating real-time endpoints through the SDK

One-click deployment through AML studio is really easy, but most organizations will require you to develop your solutions via code. Luckily, creating real-time scoring endpoints for AutoML models via the AzureML Python SDK is almost as easy as creating them through the UI. Furthermore, you'll gain a deeper understanding of how your endpoints work and how to format your JSON testing to pass data into the endpoint as a request.

In this section, you'll begin by entering your Jupyter environment and creating a new notebook. First, you will deploy your Diabetes-AllData-Regression-AutoML model via ACI, test it, and, once you've confirmed that your test is a success, create a new AKS cluster via code and deploy it there. You will conclude this section by testing your AKS deployment and confirm that everything works as expected.

The goal of this section is to further your understanding of real-time scoring endpoints, teach...