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 regression model

Compared to setting up your Jupyter environment and preparing your data, training an AutoML model involves fewer steps. First, you will need to set a name for your experiment. Remember that experiments automatically log information about your AutoML runs. Next, you will need to set your Target column, which is the column you wish to predict, and a few other settings. Finally, you will use AutoML to train a model and watch the results in real time.

In this section, you will create an experiment, configure the various parameters and settings specific to AutoML regression tasks, and train three AutoML regression models using the datasets you created in the previous section. Let's get started:

  1. Set Experiment and give it a name by using the following code. This is where all of the logs and metrics of your run will be stored in the AML studio:
    experiment_name = 'Diabetes-Sample-Regression'
    exp = Experiment(workspace=ws, name...