We’ll quickly define a ‘model on production’. There is a myriad of definitions people use right now. A variance completely justified. Because the exact way we do define it depends. Among other factors on: the size of the company, number of models, properties of the data, and so on.
While we’re at it we’ll also answer some pinning questions such as: Is it ever OK to duct-tape the model deployment process? What about using shortcuts and opting for some manual work? Pithy answers. We’ll pause to take on any questions. If there will be too many to fit, we’ll provide contact information and make sure to address all enquiries after the presentation.

Now practice makes perfect. Or nearer perfection at least. So we’ll briefly present two cases of solutions solving the same problem for two clients, both implemented with a vast difference. We’ll explain why. Both solutions required optimising search engine for results that translate into higher revenue. Yet, the companies are on opposite sides of the scale. One a huge, mature retailer, another a much younger and smaller online-booking business.
Cost must always be justifiable. So, using these cases we’ll succinctly show how to fit a solution to match the context of the organisation. How to utilise it well. Next, quickly going through some details of the models and infrastructures, we’ll explain the reasoning behind the critical decisions as well as highlight pros and cons of both approaches.