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

Registering your trained forecasting model

The code to register forecasting models is identical to the code you used in Chapter 4, Building an AutoML Regression Solution, in order to register your regression model, and in Chapter 5, Building an AutoML Classification Solution, in order to register your classification models. Always register new models, as you will use them in either real-time scoring endpoints or batch execution inference pipelines depending on your business scenario. Likewise, always add tags and descriptions for easier tracking:

  1. First, give your model a name, a description, and some tags. Tags let you easily search for models, so think carefully as you implement them:
    description = 'Best AutoML Forecasting Run using OJ Sales Sample Data.' 
    tags = {'project' : "OJ Sales", "creator" : "your name"} 
    model_name = 'OJ-Sales-Sample-Forecasting-AutoML' 
  2. Next, register your model to your AMLS workspace...