From model-centric to data-centric

In my last blog post I've covered the rise of DataPrepOps and the importance of data preparation to achieve optimized results from Machine Learning based solutions. The value of data and its impact on the quality of ML-based solutions have, for sure, been underestimated so far, but this is changing -- in Andrew's NG latest session, he covered the benefits of a bigger investment in data preparation with his team proving that investing in improved existing data quality is effective as collecting the triple amount of the data. And that is what I'll be covering today -- the role of data quality in taking AI to the next level. What is the right balance to achieve success? With the datasets publicly available, through open databases or Kaggle, for example, I understand why the more model-centric focused approach: data in its essence more or less well-behaved, which means that to improve the solutions, the focus had to be on the only element that had more freedom to be tweaked and changed, the code.