Distinguish the goals and constraints of training and inference in a production ML system.
The reframe: training is the learning factory; inference is the delivery system. Training optimizes the model During training, the system sees historical data, compares predictions with known answers or objectives, and updates parameters. The main questions are whether the data is fit for purpose and whether the learned relationship generalizes. Inference operates inside a workflow Inference happens when the trained model faces a new case. Now speed, cost, reliability, feature availability, and user experience matter. A slow but accurate model may be unusable if the workflow demands sub-second response. Production failures often live in the gap Many teams celebrate offline…
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