How to conduct model testing on a live system?

Testing models on a live system (production) requires a cautious approach to minimize risks and not disrupt user operations. Main methods:

• A/B testing: part of the traffic is directed to the new model, and part to the old one. Effectiveness metrics are compared.
• Shadow testing: the new model operates in parallel with the current one, but its decisions do not affect users. This allows quality assessment without risk.
• Canary deployment: the new model is deployed to a limited part of the system or users to test its behavior in real conditions.
• Monitoring and logging: it is important to set up metric collection and logs to quickly detect deviations and errors.

Example: when implementing a recommendation model, you can initially show recommendations from the new model to 5% of users, and the remaining 95% to the old one. By analyzing conversion and behavior, a decision is made about full deployment.