Low-Code AI

Chapter 1. How Data Drives Decision Making in Machine Learning

This chapter explores the role of data in the enterprise and its influence on business decision making. You also learn the components of a machine learning (ML) workflow. You may have seen many books, articles, videos, and blogs begin any discussion of the ML workflow with the gathering of data. However, before data is gathered, you need to understand what kind of data to gather. This data understanding can only be achieved by knowing what kind of problem you need to solve or decision you need to make.

Business case/problem definition and data understanding can then be used to formulate a no-code or low-code ML strategy. A no-code or low-code strategic approach to ML projects has several advantages/benefits. As mentioned in the introduction, a no-code AutoML approach enables anyone with domain knowledge in their area of expertise and no coding experience to develop ML models quickly, without needing to write a single line of code. This is a fast and efficient way to develop ML applications. A low-code approach enables those with some coding or deep coding experience, to develop ML applications quickly because basic code is autogenerated—and any additional custom code can be added. But, again, any ML project must begin with defining a goal, use case, or problem.

What Is the Goal or Use Case?

Businesses, educational institutions, government agencies, and practitioners face many decisions that reflect real-world examples ...