Static vs Dynamic Training and Inference

Static vs Dynamic Training

Static training involves training an algorithm in a controlled experiment, with data that doesnt change. By comparison, dynamic training involves continually learning with data that is constantly coming in. For example: if we were trying to train an algorithm that determined which songs we like, we might take a dump of data, train our algorithm, then create a website that provided those recommendations. In this system, the underlying recommendation model wouldn’t change. By contrast, a dynamic system would constantly change based on data coming in (eg: from user interactions on the Internet) and would continually change based on songs, users, and preferences that were added as time progressed.

Static vs Dynamic Inference

Static inference involves storing predictions in (for example) a database, to be retrieved by users when necessary. Conversely, dynamic inference means we run our computations at the time the data is requested.

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