What the term really means
The process in which a trained model processes input and produces a prediction, classification or answer.
How it works in practice
Inference occurs for every production model response and includes input preparation, output computation and downstream handling.
The decision to make before implementation
Infrastructure choice depends on model size, volume, latency, privacy and whether demand is steady or bursty.
How to verify that it works
Measure time to first output, end-to-end latency, cost, resource use, errors and quality after optimisations such as quantisation. Compare results with an agreed baseline and review routine cases, difficult exceptions and human hand-offs separately. A practical Inference test should have an owner, a review date and a recorded example of an outcome the team will not accept.
The PAR HOUSE Agency approach
We approach Inference from the workflow rather than a tool demonstration. Infrastructure choice depends on model size, volume, latency, privacy and whether demand is steady or bursty. We then build a small measurable scope, record assumptions and expand only after quality review.
