Intermediate · Systems

Model Deployment

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The process of making a trained machine learning model available for use in a production environment.

Technical Definition

The process of making a trained machine learning model available for use in a production environment.

How it works

Model deployment is a crucial step in the MLOps lifecycle, taking a validated model and integrating it into an application or system where it can serve predictions. This often involves packaging the model, setting up APIs, and ensuring it can handle real-time requests efficiently. Proper deployment is key to realizing the value of AI models.

Related Concepts

• Inference — Using a trained model to make predictions on new data — the deployment phase of machine learning.
• MLOps — A set of practices combining Machine Learning, DevOps, and Data Engineering to reliably and efficiently deploy and maintain ML systems.