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…

Keld Stehr Nielsen

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