Hi Maninda, With the initial release of ML, the model would not update on the fly. If we need to update the model, it has to be done in ML by training a model with the same configurations, but with the new data. In future releases we will be looking at this for possible improvements.
Here the term "domain" refers to the definition of the data which use to train the model. In the case you mentioned, If the model was trained using the data collected from all incoming requests; then domain is "all sales". and can be used to handle all incoming requests. But if the model was trained using the data collected from the incoming requests of, say Seller_A, then it cannot be used to predict/handle incoming requests of all sellers, which is a different domain. When we say "domain", it also includes the Schema (set of features/variables) of the dataset on which the prediction is going to be done. Handling multiple schema is tricky. If the data from a different domain doesn't contain some of the features (variables in the dataset) used to train the model, then that model cannot be used to predict on that particular data. So we have to work with this limitation, hence all the data has to be on the same domain (i.e. should have the same schema). Regards, Supun On Tue, Mar 24, 2015 at 3:10 PM, Maninda Edirisooriya <mani...@wso2.com> wrote: > Does this update the model time to time or use the same modal throughout > the ESB up time? It is better if we can update the model while the ESB is > up and running. > There may be some use cases related to multiple domains right? So I think > would be more generic and useful if we can apply the mediator not based on > the domain but with a custom argument expression. For example if the target > service is SalesService and the operation is getTotalSalesCount we want to > use the modal to analyze all incoming requests from all domains. WDYT? > > Thanks. > > > *Maninda Edirisooriya* > Senior Software Engineer > > *WSO2, Inc.*lean.enterprise.middleware. > > *Blog* : http://maninda.blogspot.com/ > *E-mail* : mani...@wso2.com > *Skype* : @manindae > *Twitter* : @maninda > > On Tue, Mar 24, 2015 at 12:00 PM, Manorama Perera <manor...@wso2.com> > wrote: > >> Hi, >> >> ESB mediator for WSO2 ML enables making use of the models generated by >> WSO2 Machine Leaner, to predict values in message mediation in ESB. >> >> - Machine Learner feature needs to be installed in ESB prior to >> configure the ML mediator. >> - Mediator will extract the data from the incoming messages and send >> to the ML model as features. >> - The mediator use the Machine Learner features to predict the output >> value for every message passing through the mediator. >> >> *Proposed Mediator Configuration* >> >> <ml> >> <model storage-location="string"/> >> <feature name="string" expression="xpath"/>+ >> <prediction expression="xpath"/> >> </ml> >> >> *model* >> storage-location : Storage location of the generated model. >> >> *feature* >> name : Feature name according to the generated model. >> expression : XPath expression used to determine the feature value. >> >> *prediction* >> expression : XPath expression to determine the location to insert the >> predicted value. >> >> The generated models can be applied to predict only the data coming from >> the same domain, as the data used to train the model. So the mediator >> should be configured only to mediate messages which carry data coming from >> the same domain, as the data used to train the model. >> >> Any suggestion are appreciated. >> >> Thanks. >> >> -- >> Manorama Perera >> Software Engineer >> WSO2, Inc.; http://wso2.com/ >> Mobile : +94716436216 >> >> _______________________________________________ >> Architecture mailing list >> Architecture@wso2.org >> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >> >> > > _______________________________________________ > Architecture mailing list > Architecture@wso2.org > https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture > > -- *Supun Sethunga* Software Engineer WSO2, Inc. http://wso2.com/ lean | enterprise | middleware Mobile : +94 716546324
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