Hi, Apologies for the generic question.
As I am developing predictive models for the first time and soon model will be deployed in production very soon. Could somebody help me with the model deployment in production , I have read quite a few on model deployment and have read some books on Database deployment . My queries relate to how updates to model happen when current model degenerates without any downtime and how others are deploying in production servers and a few lines on adoption of PMML currently in production. Please provide me with some good links or some forums so that I can learn as most of the books do not cover it extensively except for 'Mahout in action' where it is explained in some detail and have also checked stackoverflow but have not got any relevant answers. What I understand: 1. Build model using current training set and test the model. 2. Deploy the model,put it in some location and load it and predict when request comes for scoring. 3. Model degenerates , now build new model with new data.(Here some confusion , whether the old data is discarded completely or it is done with purely new data or a mix) 4. Here I am stuck , how to update the model without any downtime, the transition period when old model and new model happens. My naive solution would be, build the new model , save it in a new location and update the new path in some properties file or update the location in database when the saving is done. Is this correct or some best practices are available. Database is unlikely in my case. Thanks in advance.
