When using ml-engine for online prediction we send a request and get the prediction results, that's cool but Request is usually different compared to model input, for example:
- A categorical variable can be in request but model is expecting and integer mapped to that that category
- also for a given feature we may need to create multiple features, like splitting text into two or more features
- And we might need to exclude some of the features in the request like a constant feature that's useless for the model
How do you handle this process? My solution is to get the request with an appengine app, send it to pub/sub , process it in dataflow, save it to gcs and trigger a cloud function to send the processed request to ml-engine endpoint and get the predicted result. This can be an over-engineering and I want to avoid that, If you have any advice regarding to Xgboost models I'll be appreciated.
