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

Visualizing AutoML modeling results

Presenting the results of your AutoML model to your business is integral to the adoption of your solution. After all, your end users are unlikely to adopt your solution unless they can be sure that it meets certain standards of performance. There are many ways of presenting the results of ML models; the most effective way of presenting your results is through visualizations.

Thankfully, AutoML runs provide automatic visualizations for results of regression, classification, and forecasting. Regression and forecasting share identical visualizations, while classification is quite different. In each case, you only want to share a single visualization with your end user; multiple views of the same results are likely to only cause confusion.

In this section, you'll first uncover what to show your end user for classification before moving onto regression and forecasting.

Visualizing the results of classification

Confusion matrices, as...