Part 2: The Building Blocks of Data-Centric ML
In this part, we lay the groundwork for data-centric ML with four key principles that underpin this approach, giving you essential context before exploring specific techniques. Then we explore human-centric and non-technical approaches to data quality, examining how expert knowledge, trained labelers, and clear instructions can enhance your ML output.
This part has the following chapters:
- Chapter 3, Principles of Data-Centric ML
- Chapter 4, Data Labeling Is a Collaborative Process