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

Training many models simultaneously

Like prepping data for many models, training many models is simply a matter of navigating to the correct notebook and running the cells. There's no custom code required, and you are simply required to change a few settings.

Like prepping data, you will first run the notebook step by step to carefully understand how it works. Once you have that understanding, you will then create a new notebook with code that uses the datasets you made from the sample data. This will benefit you tremendously, as you will understand exactly which parts of the code you need to change to facilitate your own projects.

Training the sample OJ dataset

To train many models using the OJ data and to understand the underlying process, follow these instructions step by step:

  1. From the solution-accelerator-many-models folder, click on the Automated_ML folder.
  2. From the Automated_ML folder, click on the 02_AutoML_Training_Pipeline folder.
  3. Open 02_AutoML_Training_Pipeline...