Summary
In this chapter, we outlined the four principles of data-centric ML. By following these principles, you will be able to create ML models that are based on high-quality data that has been enhanced, cross-checked, and verified by humans, labeling functions, and ML techniques.
This allows us to get more signals out of our data, which, in turn, increases our ability to build powerful models on small or large datasets. Lastly, we can capture ethical considerations throughout the development life cycle, which ultimately ensures we’re using our powers for good.
In the next chapter, we’ll explore specific ways you can structure, optimize, and govern the process of using human annotators for your ML projects.
