Most AutoML solutions are developed with a model-centric approach, however, according to a research paper, “AutoDC Automated Data-centric Processing,” that was accepted into last year’s highly selective NeurIPS conference on the development of an automated data-centric tool (AutoDC), it was found to save an estimated 80% of the manual time needed for data set improvement – typically a bespoke and costly process.
One of the authors, Zac Yung-Chun Liu, Chief Data Scientist at Hypergiant and part-time research associate at Stanford, is an expert in the field of AutoML. The paper offers a perspective for practical data-centric AutoML solutions, and how this new no-code and low-code solution is one of many R&D efforts that will push the field forward.
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