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 an AutoML classification model

Training an AutoML classification model is very similar to training an AutoML regression model, but there are a few key differences. In Chapter 4, Building an AutoML Regression Solution, you began by setting a name for your experiment. After that, you set your target column and subsequently set your AutoML configurations. Finally, you used AutoML to train a model, performed a data guardrails check, and produced results.

All of the steps in this section are nearly the same. However, pay close attention to the data guardrails check and results, as they are substantially different when training classification models:

  1. Set your experiment and give it a name:
    experiment_name = 'Titanic-Transformed-Classification'
    exp = Experiment(workspace=ws, name=experiment_name) 
  2. Set your dataset to your transformed Titanic data:
    dataset_name = "Titanic Transformed"
    dataset = Dataset.get_by_name(ws, dataset_name, version=&apos...