let's say it's a window of 60 days (so I provide 60 records), and a prediction interval of 14 days (so I get returned 14 records)
is the time aware platform meant to be used like this (making a prediction for a single subject/single prediction at a time)? It feels so jarring, considering that the not-time-aware seems conducive to making millions of predictions at a time.
follow up question is what kind of big-data/large amount of prediction use cases there are for time series that can be done on this platform?
While I'm consulting over your mlflow question, let me try to help you with understanding time aware models.
You are correct about your estimations in example given, but there are few things are missing:
1) Series ID in multi-series setup. Your example described only one series, while you can do predictions to many in parallel. Also there is segmented modeling to scale up this parameter to really high values.
2) You can provide more than 60 values of historical data for prediction, let's say 60+n, then you can have forecast range predictions which will be able to give 14 predictions for up to n forecast points, depending on size of your range.
Time-aware models provide you with automatic time series feature derivations and proper time-aware validation methodic. If you are sure of all features that are needed and enough for your project, you can use OTV modeling and handle modeling in a way you see it fits the best.
Generally speaking, DataRobot has a lot of things created for variability of predictions, time-aware modeling is usually made for 1 forecast point for the latest data as there is no interest in having overlapping predictions that may have actuals already available.