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Machine learning has moved from prototype to production across a wide range of business units at financial services giant Capital One due in part to a centralized approach to evaluating and rolling out new projects. This is no easy task given the scale and scope of the enterprise but according to Zachary Hanif who is director of Capitol One's machine learning "center for excellence", the trick is to define use cases early that touch as broad of a base within the larger organization as possible and build outwards. This is encapsulated in the philosophy Hanif spearheads--locating machine learning talent in one repository that can branch out and work with the experts across the many business divisions. Hanif shared these and other lessons for building a machine learning hub inside a large enterprise where purely machine learning experts work with the different domain and departmental efforts to roll new services into production at the GPU Technology Conference (GTC18). While GPUs were not necessarily the topic of the talk by any means, Hanif did say they have quite a number along with standard CPU based clusters and just like any other enterprise or academic center with a wide range of mission-critical R&D projects on the burner, resource contention is a constant struggle, especially when it comes to the more rare and expensive GPUs they have.