Model Drift
AI Infrastructure
The process of making machine learning models available for use in a production environment.
Detailed Explanation
Model deployment involves integrating a trained machine learning model into a production environment where it can serve real-time predictions or insights. This process ensures that the model functions efficiently, reliably, and securely for end-users, often requiring considerations like scalability, latency, monitoring, and maintenance to support ongoing performance and updates in practical applications.
Use Cases
- •Use case: Deploying a fraud detection model to monitor transactions in real-time, alerting users instantly of suspicious activity.
Related Terms
Other terms in the AI Infrastructure category