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

Working with data in AMLS

Now that you've created a compute, all you need to do is create a dataset and you will be ready to run your first AutoML job. Datasets are simply pointers to files on your Storage account or pointers to SQL queries on Azure SQL databases.

A dataset is not a file itself. You can create datasets from local files, from SQL queries, or from files in your storage accounts. Azure Open Datasets, publicly available data curated by Microsoft, can also be registered as datasets. For this exercise, we will create a dataset using the Diabetes open dataset.

Creating a dataset using the GUI

Let's begin:

  1. Click the Dataset tab.
  2. Click the Create dataset button, indicated by the blue cross, and you will be presented with a dropdown. Select From Open Datasets, as shown in the following screenshot.

    Note that you can also use this dropdown to create datasets from local files on your computer, from web files, or from data found in your datastores...