Hi 🐸 folks, When using unsupervised learning algorithms (like K-Means) we need to save the predicted labels (cluster IDs) for the training data back into the datastore. Ideally, we want to automatically save bulk predictions for the training data after the model is created, when the RDD/DataFrame of all that data is already resident in Spark memory. It seems complex & inefficient to develop a whole separate process that (re)selects all that training data and then iteratively POSTs to `/queries.json` to get every prediction…
Would adding a `bulk_save_predictions()` function to the persistent model's #save method might be the right place to save predictions back into the eventdata store? How do you folks label the training data from an unsupervised algorithm? Any suggestions for making bulk predictions that mesh with PredictionIO's workflow? *Mars Hall Customer Facing Architect Salesforce App Cloud / Heroku San Francisco, California
