With the amount of time and energy that’s spent on perfecting and creating an analytic model, you’d think it would come at the hands of a wizard or sorceress. Unfortunately, there’s no magic wand that can successfully bridge IT and data science teams when it comes to model deployment.These models are created to generate insights that enable us to make better decisions, faster. To maintain that competitive edge that’s crucial to survive, the value of the data generated must be recognized as quickly as possible to be relevant, and it must always remain as accurate as possible.This can be an easy task when you’re working with one model, but as systems, problems and team complexity increases, this task can be paralyzing, not to mention cost-prohibitive, for an organization. I’ve spent the past few articles on this column and in our research at Open Data Group detailing just how important it is for the data science and IT teams find solutions that allow them to come together every time a model is pushed into production. The importance of this step can’t be overstated. It’s a process that is further complicated when making updates to the model or the system surrounding it.To read this article in full, please click here

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