I am trying to build price recommendation solution for clients in a scalable manner. I have two choices as below.
Professional service: Statistician involvement to build regression model or any other kind of predictive model that fits specifically to client data and can be used.
Issue: So on the long run there will be issues around scalability as one analyst cannot build model simultaneously for hundreds of clients who want to come on board and use this service. Hiring 1 analyst for 100 clients is not an option for me as i do not run this operation.
Product: Plug and play with least human intervention. i.e. Some kind of self learning/artificial algorithm that works for any and every client. Additionally, it adjusts to new trends that appear over time which might not have been captured in the existing model. Personally, i will be biased towards this kind of solution.
Issue: This is the ideal solution, however i am not sure how to build this and how to approach in this direction.
To give context, a client in Europe might have same data structure in their sql database in terms of name, variable type in comparison to sql database of a client who is based in Singapore. However, Singapore data might have different trend, seasonality and pattern in comparison to data in Europe. Is there any way that a single Machine Learning or Artificial Intelligence or Deep Learning or Neural Net algorithm can be built and used for all regions and clients without having to update that model manually for any new client?
My background: i have previously worked in a professional service landscape and have statistics/predictive modelling background.