The Principles of a Data-Centric Approach to AI

There is no shortage of hype around AI — and with good reason. AI aims to redefine how information is collected, integrated, and analyzed; ultimately leading to more informed insights and delivering better outcomes. But for all its hype, integrating AI remains a challenge for many companies. While the media focuses on models and algorithms, the data aspect is not as prominently discussed. According to a recent paper by Google researchers, “data is the most under-valued and de-glamorized aspect of AI.”

If you are reading this whitepaper, you clearly understand the importance of data — it is what makes artificial intelligence truly intelligent. The challenge is getting the right data to continuously train your machine learning models. Successful AI projects demand an accurate foundation on which to build and train machine learning algorithms. In this paper, we will reveal the best framework to build a sustainable, quality data pipeline to ensure the success of your AI projects. Then we will examine the newest data-centric strategy and why it is essential for building a high performing AI model. Finally, you will find a curated list of resources to source relevant data for your unique use cases.