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

Building compute to run your AutoML jobs

The first time you open AML studio, navigate to the Compute tab to create a compute instance and a compute cluster. Once you open the tab, you will see four headings at the top: Compute instances, Compute clusters, Inference clusters, and Attached compute. Let's take a look at these in more detail:

  • Compute instances are virtual machines that you can use to write and run Python code in Jupyter or JupyterLab notebooks; you can also use a compute instance to write R code using R Studio.
  • Compute clusters are groups of virtual machines used to train ML models remotely. You can kick off jobs on a compute cluster and continue working on code in your compute instance.
  • Inference clusters are groups of virtual machines used to score data in real time.
  • Attached compute refers to using Databricks or HDInsight compute to run big data jobs.

Let's see them in action.

Creating a compute instance

We'll start...