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 UI

The crux of any real-time scoring solution is a real-time scoring endpoint, a web URL through which you can pass data and immediately retrieve ML predictions. Endpoints are hosted on containerized services that are up and running 24 hours a day, 7 days a week, waiting for incoming requests.

Requests send data to the endpoint for scoring and can be written in any computer language including Python. As soon as a request comes through, your endpoint will automatically execute the underlying code and return results.

You can use these endpoints anywhere; any coding language from C# to Python to Java can make use of real-time scoring endpoints. Thus, once you obtain the URL that hosts the endpoint, you are free to implement it in any other piece of code. Commonly, real-time scoring endpoints are incorporated in streaming jobs, web applications, and mobile apps.

When using real-time scoring endpoints based on AutoML models, there are...