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

Technical requirements

This chapter will feature a lot of coding using Jupyter notebooks within AMLS. Thus, you will need a working internet connection, an AMLS workspace, and a compute instance. ML pipelines also require a compute cluster. You will also need to have trained and registered the Iris multiclass classification model in Chapter 5, Building an AutoML Classification Solution.

The following are the prerequisites for the chapter:

  • Access to the internet.
  • A web browser, preferably Google Chrome or Microsoft Edge Chromium.
  • A Microsoft Azure account.
  • Have created an AMLS workspace.
  • Have created the compute-cluster compute cluster in Chapter 2, Getting Started with Azure Machine Learning Service.
  • Understand how to navigate to the Jupyter environment from an Azure compute instance as demonstrated in Chapter 4, Building an AutoML Regression Solution.
  • Have trained and registered the Iris-Multi-Classification-AutoML ML model in Chapter 5, Building...