What is model deployment?

• A. Validating model on the training data only.
• B. Retraining the model on a daily basis without evaluation.
• C. Testing the model locally with no production impact.
• D. Integrating the trained model into production systems.

Answer: (D)

Model deployment is the step where a trained model is made available for real use in a production environment. It means embedding the model into an application or service so it can receive input data, run predictions, and deliver outputs to users or other systems—whether in real time or in scheduled batch processes. Deployment also involves setting up a stable serving endpoint, versioning the model, ensuring scalable and low-latency inference, integrating with data pipelines, and monitoring performance and data drift to catch issues early.

The other options describe activities that happen before or outside deployment: validating on training data is part of model validation, not deploying it; retraining daily without evaluation lacks a deployment context and proper checks; testing locally with no production impact is simply testing, not putting the model into production.