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Data Science & ML

Model Serving

/ˈmɒdəl ˈsɜːvɪŋ/

Definition

Deploying a trained ML model as an API endpoint so applications can request predictions in real or batch time.

Example in context

"The recommendation model is wrapped in a FastAPI server — the product service calls /predict with user context."

Related terms

  • Inference — The process of running new input data through a trained model to generate predictions.
  • MLOps — Machine Learning Operations — combining ML development with DevOps practices for reliable model deployment and monitoring.
  • Latency — The time delay between sending a request and receiving the first byte of a response, measured in milliseconds.

Practice this term

Master Model Serving in context by working through exercises in the Data Science & ML module. You'll see the term used in real engineering scenarios with multiple-choice, fill-in-the-blank, and matching drills.

Practice in exercises → Browse all words

Frequently Asked Questions

What does "Model Serving" mean?

Deploying a trained ML model as an API endpoint so applications can request predictions in real or batch time.

How do you pronounce "Model Serving"?

"Model Serving" is pronounced /ˈmɒdəl ˈsɜːvɪŋ/.

Can you use "Model Serving" in a sentence?

"The recommendation model is wrapped in a FastAPI server — the product service calls /predict with user context."