RAPIDMINER 9.7 BETA ANNOUNCEMENT
The beta program for the RapidMiner 9.7 release is now available. Lots of amazing new improvements including true version control!
RapidMiner best practices for multiple customers/models
What are some of the best practices which we can follow when we use RapidMiner server for multiple customers.
Suppose I have a RapidMiner server and the training and prediction processes are exposed as web services. The processes have been parameterized(using macros) to dynamically handle training/prediction for different customers. There are two models per customer.
1. How to manage when the number of models becomes huge. Currently, we have one folder per customer.
2. How to handle the load when we have multiple prediction processes being invoked. Should we use multiple RapidMiner servers? Is there any scaling mechanism ( auto-scaling) to scale the training/prediction process horizontally?