Model Serving
Definition
Model deployment makes a trained model usable for applications.
The technical operation of a trained model as an on-demand prediction function, batch process, or embedded component.
Simply explained
The fully trained AI component is connected so that other programs can use it.
Why it matters in practice
Helps develop and operate digital systems robustly, securely, and maintainably.
Example from business
A model runs behind a versioned API with scaling, authentication, and monitoring.
Limits and pitfalls
- Only consider the endpoint and forget preprocessing, versions, fallback, and SLOs.
Origin & history
Model serving evolved from the production operation of statistical and ML models.
Use in mid-sized companies
- Possible example: A model runs behind a versioned API with scaling, authentication, and monitoring.
Frequently asked questions
What does model deployment mean?
The technical operation of a trained model as an on-demand prediction function, batch process, or embedded component.
Which mistakes should be avoided?
Only consider the endpoint and forget preprocessing, versions, fallback, and SLOs.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: