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

Realizing business value

Realizing business value ultimately comes down to whether your business partners choose to act on the predictions of your ML models. Without action, the work of data scientists amounts to little more than a science experiment. Your business partners must be motivated and willing to make your predictions a part of their decision-making process. Gaining their trust is paramount.

In order to gain the trust of your company's decision-making leadership, you first have to ascertain what kind of solution you are building with AutoML. Some solutions are rather easily and rapidly adopted, while others are likely to encounter hard resistance.

There are key two factors that determine how readily your AutoML solution is accepted: whether your tool is replacing an existing solution and whether your tool is directly involved in an automated decision process or is assisting human decision makers. Figure 12.8 shows how difficult it is to gain acceptance based...