Summary

In this chapter, we reviewed the history of ML to give us a clear understanding of why model-centric ML is the dominant approach today. We also learned how a model-centric approach limits us from unlocking the potential value tied up in the long tale of ML opportunities.

By now, you should have a strong appreciation for why data-centricity is needed for the discipline of ML to achieve its full potential but also recognize that it will require substantial effort to make the shift. To become an effective data-centric ML practitioner, old habits must be broken and new ones formed.

Now, it’s time to start exploring the tools and techniques to make that shift. In the next chapter, we will discuss the principles of data-centric ML and the techniques and approaches associated with each principle.