Background - I'm creating a time-series anomaly detection (TSAD) model for the wifi throughput. My customers are 2 banks, 5 retail stores, 4 universities, 6 hospitals. Currently, I have 2 options to design my solution.
Separate TSAD model for each customer - I create 17 models, one for each customer. This might be most accurate but impossible to scale
Industry specific TSAD model - I create 4 models. In this case, I will merge the data for 2 banks to generate 1 TSAD model. Similarly, I will merge data from 5 retail stores to generate 1 TSAD model. By repeating the process for other 2 industry verticals, I get 2 more TSAD models. This solution is better than 1. but still not great. Scalability is still a challenge because I need to maintain, update, monitor, version etc. 4 models. Moreover, the number of models will increase if model gets deployed in other industries such as Petrochemical.
Question - I would like to know is there a way for me to create one TSAD model that works for all industires. That will be truly scalable as I need to maintain, update, monitor, version one model only irrespective of the number of industries it is deployed in.