I feel pretty good about this question. From my understanding, the most common machine learning deployment paradigm is batch processing, where models are deployed to run on a regular schedule rather than in real-time or on-device. I'll go with that.
Okay, I've got a strategy for this. I'll start by eliminating the options that I'm sure are not the most common, then focus on the remaining ones and try to determine the best answer.
Hmm, I'm a bit unsure about this one. The different deployment options seem similar, and I'm not entirely confident in my knowledge of the most common approach. I'll have to think this through carefully.
This seems like a straightforward question about common machine learning deployment paradigms. I'll review the options and try to recall which one is typically the most common.
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