Until recently, large financial services organizations on boarded data science teams, placed them next to the delivery organization and gave them tools for experimentation. It was as if every organization had to demonstrate for themselves that their data combined with statistical modelling had potential to generate business insights. When the derived insights failed to translate into actual, running models continuously delivering business value, it generated frustration among business leaders. The recent interest in applied machine learning tooling, including MLOps and DataOps, is a recognition that there is a commercial potential to harvest by remedying this executive frustration.

The lesson is that hiring a group of data scientists is no guarantee for commercial success. At minimum, they must engage in Applied Machine Learning and carefully balance any experimentation or research conducted outside that paradigm. Applied Machine learning is an engineering discipline aimed at deploying machine learning systems that solve particular real world problems.[1] Opposed to this stand Machine Learning Research, which experiments in the pursuit of better algorithms or explores the frontiers of industry application. The key principle of Applied Machine Learning is to think execution before experimentation — think of how to execute before what to execute.[2]

Keld Stehr Nielsen

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