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

For this chapter, you will be building models with Python code in Jupyter notebooks through Azure Machine Learning (AML) studio. Furthermore, you will be using datasets and Azure resources that you should have created in previous chapters. As such, the full list of requirements is as follows:

  • Access to the internet
  • A web browser, preferably Google Chrome or Microsoft Edge Chromium
  • A Microsoft Azure account
  • An Azure Machine Learning workspace
  • The titanic-compute-instance compute instance created in Chapter 2, Getting Started with Azure Machine Learning
  • The compute-cluster compute cluster created in Chapter 2, Getting Started with Azure Machine Learning
  • The Titanic Training Data dataset from Chapter 3, Training your First AutoML Model
  • An understanding of how to navigate to the Jupyter environment from an Azure compute instance as demonstrated in Chapter 4, Building an AutoML Regression Solution