Even a well-trained model has no value by itself. It must be deployed so users can call it, accumulated data gets batch-processed, or other systems invoke it—only then does it create business value. SageMaker offers 4 inference modes fitting different workload shapes, and MLA-C01 exams repeatedly ask "which mode for this scenario".
The key is not algorithm but request pattern. Do requests come realtime or batched, is traffic steady or sparse, are responses needed immediately or can they tolerate minute delays, are payloads small or huge. Dividing by these 4 axes solves nearly every test question. Today we compare 4 options at a glance and organize selection criteria.