No, both engines can even use the same data. Two engines is far simpler to construct and manage. You make 2 queries but internally with multiple algorithms 2 queries would be made also. The only thing you save is having 2 deployed engine processes rather than having them in the same single process at the cost if complexity.
On Feb 28, 2017, at 6:11 PM, Kanak Singh <[email protected]> wrote: This way, I can deploy one engine for one user of my web app, and that can take care of all prediction needs of that user. If I deploy multiple engines for one user, it will limit the number of users I can have, right? On Tue, Feb 28, 2017 at 4:49 PM, Kanak Singh <[email protected] <mailto:[email protected]>> wrote: This way, I only have to deploy that one engine, instead of deploying multilpe ones. On Tue, Feb 28, 2017 at 4:19 PM, Pat Ferrel <[email protected] <mailto:[email protected]>> wrote: May I ask why you are packaging these as multiple algorithms in a template rather than multiple templates with one algo each? On Feb 28, 2017, at 3:29 PM, Kanak Singh <[email protected] <mailto:[email protected]>> wrote: Hi all, This is my first email so pardon me for any mistakes in email etiquette. I am trying to create a multi purpose engine in which I can include multiple algorithms that operate on multiple data sets, and that is what I need some help with. I have read the documentation on including multiple algorithms and handling multiple events. I see that the DataSource and Preparator can collect data across different channels/ different event types and pass it on as individual RDD[LabeledPoint] fields of the PreparedData object. And I can add multiple Algorithm files and have each of them access the respective PreparedData field that I choose. However, for this I have to decide which algorithms I can offer to the client and how many of them, at the time of creating an Engine template folder. I am trying to go one step further by making one engine handle an unspecified number of data sets (dynamically uploaded by client using POST requests) and operate on them using any algorithm (specified perhaps in a 'pio train --algorithm' option). First of all, is this a good idea? If so, what is the best way to do it? Any help or leads would be appreciated. Best.
