How Data-Centric Platforms Solve the Biggest Challenges for MLOps

Recently, I learned that the failure rate for machine learning projects is still astonishingly high. Studies suggest that between 85-96% of projects never make it to production. These numbers are even more remarkable given the growth of machine learning (ML) and data science in the past five years. For businesses to be successful with ML initiatives, they need a comprehensive understanding of the risks and how to address them. In this post, we attempt to shed light on how to achieve this by moving away from a model-centric view of ML systems towards a data-centric view. Of course, everyone knows that data is the most important component of ML. Nearly every data scientist has heard: "garbage in, garbage out" and "80% of a data scientist's time is spent cleaning data".