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

Chapter 12: Realizing Business Value with AutoML

You have acquired a wide variety of technical skills throughout this book. You're now able to train regression, classification, and forecasting models with AutoML. You can code AutoML solutions in Python using Jupyter notebooks, you know how to navigate Azure Machine Learning Studio, and you can even integrate machine learning pipelines in Azure Data Factory (ADF). Yet, technical skills alone will not guarantee the success of your projects. In order to realize business value, you have to gain the trust and acceptance of your end users. 

In this chapter, you will begin by learning how to present end-to-end architectures in a way that makes it easy for end users to understand. Then, you will learn which visualizations and metrics to use to show off your model's performance, after which you will learn how to visualize and interpret AutoML's built-in explainability function.

You will also explore options to run...