>From my personal experience - we're reading the metadata of the features
column in the dataframe to extract mapping of the feature indices to the
original feature name, and use this mapping to translate the model
coefficients into a JSON string that maps the original feature names to
their
Let's suppose you have trained a LogisticRegressionModel and saved it at
"/tmp/lr-model". You can copy the directory to production environment and
use it to make prediction on users new data. You can refer the following
code snippets:
val model = LogisiticRegressionModel.load("/tmp/lr-model")
val
Hi all,
How do you reliably deploy a spark model in production? Let's say I've done
a lot of analysis and come up with a model that performs great. I have this
"model file" and I'm not sure what to do with it. I want to build some kind
of service around it that takes some inputs, converts them
