Deployment Introduction
Inference & deployment
Use a trained model to make predictions in a real setting.
Before this
The intuition
Inference applies a trained model to new inputs. Deployment makes that computation available in a product or process, together with the preprocessing it expects.
A concrete example
A model trained on standardized inputs must receive inputs transformed with the saved training statistics. Sending raw values changes what its weights mean.
What to remember
Accuracy is only part of deployment. Input compatibility, latency, and changes in real-world data also matter.
Check your understanding
Should training-time feature order be preserved during inference?
Reveal explanation
Yes. Swapping features sends values to the wrong learned weights.