Conventional wisdom holds that you need large volumes of labeled training data to unlock value from powerful AI models. For the consumer internet companies where many of today's AI models originated, this hasn't been difficult to obtain. But for companies in other sectors -- such as industrial companies, manufacturers, health care organizations, and educational institutions -- curating labeled data in sufficient volume can be significantly more challenging. Over the past few years, AI practitioners and researchers have developed several techniques to significantly reduce the volume of labeled data needed to build accurate AI models. Using these approaches, it's often possible to build a good AI model with a fraction of the labeled data that might otherwise be needed.

社内勉強会でAndrew Ng先生が提唱するData-Centric AIについて紹介した際の資料です。

Two basic components of all AI systems are Data and Model, both go hand in hand in producing desired results. In this article we talk about how the AI community has been biased towards putting more effort in the model, and see how it is not always the best approach. We all know that machine learning is an iterative process, because machine learning is largely an empirical science. You do not jump to the final solution by thinking about the problem, because you can no easily articulate what the solution should look like. Hence you empirically move towards better solutions.

Google researchers found that "data cascades -- compounding events causing negative, downstream effects from data issues -- triggered by conventional AI/ML practices that undervalue data quality… are pervasive (92% prevalence), invisible, delayed, but often avoidable." Lets discuss on the trend being followed widely for most or all of the AI use cases across the organizations. Just to make myself clear the term AI over here in being referred as an umbrella encompassing our DataScience/Machine Learning and Deep Learning Use cases. The Two basic components of all AI systems are Data and Model, both go hand in hand in producing desired results. We do realize that the AI community has been biased towards putting more effort in the model building One plausible reason is that AI industry closely follows academic research in AI.

Andrew Ng is a key reference point for me in understanding AI. Andrew Ng is always easy to understand – especially for new and complex ideas. Recently, Andrew has been proposing the idea of MLOps from model centrc to data centric. Why is this still not clear to me? And for AI practitioners, MLOps is not new.