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

Creating an AutoML solution

Now that you have loaded Titanic data into your datastore and registered it as a dataset, you are ready to train an AutoML model with a few guided clicks:

  1. To get started, click Automated ML from the left-hand menu under Author. Then, click New Automated ML run, marked by a blue cross, near the top left of the new page, as shown in Figure 3.7:

    Figure 3.7 – Beginning your AutoML training run

  2. Once you have advanced to the next screen, you will be presented with all of your eligible datasets for training. Currently, only tabular datasets are supported for runs from the AutoML GUI. You can also create a new dataset from this view by clicking the Create dataset button. Select Titanic Training Data, as shown in Figure 3.8.
  3. Click Next:

    Figure 3.8 – Selecting your dataset for training

    After selecting your dataset, the next steps involve naming your experiment, selecting a column to predict, and selecting a compute cluster for remote training...