Chapter 1: Introducing AutoML

AI is everywhere. From recommending products on your favorite websites to optimizing the supply chains of Fortune 500 companies to forecasting demand for shops of all sizes, AI has emerged as a dominant force. Yet, as AI becomes more and more prevalent in the workplace, a worrisome trend has emerged: most AI projects fail.

Failure occurs for a variety of technical and non-technical reasons. Sometimes, it's because the AI model performs poorly. Other times, it's due to data issues. Machine learning algorithms require reliable, accurate, timely data, and sometimes your data fails to meet those standards. When data isn't the issue and your model performs well, failure usually occurs because end users simply do not trust AI to guide their decision making.

For every worrisome trend, however, there is a promising solution. Microsoft and a host of other companies have developed automated machine learning (AutoML) to increase the success of your AI projects. In this book, you will learn how to use AutoML on Microsoft's Azure cloud platform. This book will teach you how to boost your productivity if you are a data scientist. If you are not a data scientist, this book will enable you to build machine learning models and harness the power of AI.

In this chapter, we will begin by understanding what AI and machine learning are and explain why companies have had such trouble in seeing a return on their investment in AI. Then, we will proceed into a deeper dive into how data scientists work and why that workflow is inherently slow and mistake-prone from a project success perspective. Finally, we conclude the chapter by introducing AutoML as the key to unlocking productivity in machine learning projects.

In this chapter, we will cover the following topics:

• Explaining data science's ROI problem
• Analyzing why AI projects fail slowly
• Solving the ROI problem with AutoML