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Which one of the following statements is true of dynamic (online) training?
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 inference time.
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.