Which one of the following statements is true of dynamic (online)
The model stays up to date as new data arrives.
This is the primary benefit of online training—we can avoid many
staleness issues by allowing the model to train on new data as
it comes in.
Very little monitoring of training jobs needs to be done.
Actually, you must continuously monitor training jobs to ensure that
they are healthy and working as intended. You'll also need
supporting infrastructure like the ability to roll a model back
to a previous snapshot in case something goes wrong in training,
such as a buggy job or corruption in input data.
Very little monitoring of input data needs to be done at
Just like a static, offline model, it is also important to
monitor the inputs to the dynamically updated models. We are
likely not at risk for large seasonality effects, but sudden,
large changes to inputs (such as an upstream data source going
down) can still cause unreliable predictions.