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

Prepping data for many models

While training thousands of ML models simultaneously sounds complicated, the MMSA makes it easy. The example included in the notebooks uses the OJ Sales data you used in Chapter 6, Building an AutoML Forecasting Solution. You will prepare the data simply by opening and running 01_Data_Preparation.ipynb. By reading the instructions carefully step by step and working through the notebook slowly, you will be able to understand what each section is about.

Once you're able to understand what each section is doing and you have the OJ Sales data loaded, you will be able to load the new dataset into your Jupyter notebook. This way, by the end of this section, you will be able to load your own data into Azure, modify it for the MMSA, and master the ability to use this powerful solution.

Prepping the sample OJ dataset

To understand how the first notebook works, follow these instructions in order:

  1. Open 01_Data_Preparation.ipynb.
  2. Run all...