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

Chapter 11: Implementing a Real-Time Scoring Solution

While most machine learning (ML) projects involve batch scoring, the most complex ML projects use real-time solutions. Think about models that determine whether a credit card transaction is fraudulent, models that decide which ads to show online shoppers, and models that decide whether a customer at a car dealership is creditworthy or not. These situations all demand a real-time scoring solution and it's incredibly important that your model be both fast and accurate.

Luckily, creating a fast, reliable real-time scoring solution in AutoML is easy whether you decide to code it with Python or use the Azure Machine Learning (AML) Studio graphical user interface (GUI).

You will begin this chapter by creating a real-time scoring endpoint through the AML studio GUI. Real-time scoring endpoints are web services through which you can pass data and quickly receive results. Continuing, you will then create real-time scoring endpoints...