I’ve given a version of this talk at all three EARL conferences this year starting in San Francisco. I think this is a measure of the extent to which I believe bridging the gap between Data Science and Ops is becoming increasingly important.
There is a perception in many Ops teams that Data Science lives outside the organisational mainstream, and to some extent or other “gets away” with doing what it likes. This can lead to conflict and distrust, and without the support of a great Ops team, it can be hard for Data Science teams to deliver. One way to bridge this divide is with a documented Operating Model. In this talk I describe the type of benfits both sides can get from having an operating model in place and how to start down the path to implementing one.