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

Triggering and scheduling your ML pipelines

One of the biggest problems data scientists face is creating easy, rerunnable, production-ready code and scheduling it in an automatic, reliable manner. You've already accomplished the first part by creating your three ML pipelines. Now, it's time to learn how to do the second part.

In this section, you will first learn how to manually trigger the pipelines you've created through the GUI. Then, you will learn how to trigger the pipelines via code, both manually and on an automated schedule. This will enable you to put your ML pipelines into production, generating results on an hourly, daily, weekly, or monthly basis.

Triggering your published pipeline from the GUI

Triggering your published pipeline from the AML studio GUI is easy. However, you cannot set up an automated schedule for your ML pipelines at this time. As such, it is most useful for triggering training pipelines when you notice that your results seem off...