Model Serving
AI Infrastructure
The continuous tracking of deployed AI models to ensure they maintain performance and reliability in production.
Detailed Explanation
Model Monitoring involves continuously tracking the performance, accuracy, and reliability of AI models after deployment. It detects issues like model drift or degradation, ensuring models remain effective and trustworthy in real-world environments. This process helps maintain optimal performance, facilitates timely updates, and prevents potential failures or biases from impacting decision-making systems.
Use Cases
- •Use case: Detects model drift in a credit scoring system, prompting updates to prevent bias and maintain accurate risk assessments.
Related Terms
Other terms in the AI Infrastructure category