Data-centric Machine Learning: Making customized ML solutions production-ready
By 2021, there is little doubt that Machine Learning (ML) brings great potential to today’s world. In a study by Bitkom , 30% of companies in Germany state that they have planned or least discussed attempts to leverage the value of ML. But while the companies’ willingness to invest in ML is rising, Accenture estimates that 80% – 85% of these projects remain a proof of concept and are not brought into production. Therefore at dida, we made it our core mission to bridge that gap between proof of concept and production software, which we achieve by applying data-centric techniques, among other things. In this article, we will see why many ML Projects do not make it into production, introduce the concepts of model- and data-centric ML, and give examples how we at dida improve projects by applying data-centric techniques.