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)
1
Section 1: AutoML Explained – Why, What, and How
5
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
10
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Scheduling a machine learning pipeline in ADF

Perhaps ADF's best feature is its ease of use. By clicking and dragging objects across a screen, you can easily orchestrate a flow of seamless data ingestion, transformation, and ML through an ADF pipeline. Moreover, with a few more clicks, you can schedule that ADF pipeline to run whenever you want. Gaining this skill will enable you to create code-free data orchestration runs quickly and easily.

First, you will schedule and run the simplest ML pipeline you created in Chapter 9, Implementing a Batch Scoring Solution, the Iris-Scoring-Pipeline. To do so, follow these steps:

  1. Navigate to your ADF resource and click Author & Monitor.
  2. Click the pen icon on the left-hand side. When you hover over this icon, the word Author will appear to indicate which section you're navigating to.
  3. Click the blue cross icon next to the search box under Factory Resources in the top-left corner. When you hover over this icon,...