AI & Data
Best Practice: Continuously monitor model performance in production
Sep 12, 2024
As AI models are deployed into production, their performance can degrade over time due to changing data patterns, a phenomenon known as "model drift." Continuously monitoring model performance helps detect when a model's accuracy, precision, or other key metrics start to deviate from expected levels. Monitoring is essential for maintaining reliable AI systems that adapt to new data.
Why Continuous Monitoring Matters
- Detecting model drift: As real-world data evolves, models can become less accurate. Continuous monitoring helps identify performance degradation early, allowing teams to take corrective action before it affects decision-making.
- Ensuring business value: If a model’s performance declines, its predictions may no longer provide value to the business. Monitoring ensures that models continue delivering results that align with business objectives.
- Maintaining compliance and standards: In regulated industries, continuous monitoring is often required to ensure models meet performance and fairness standards. Ongoing evaluation helps meet these compliance requirements.
- Proactive model updates: Monitoring allows teams to react proactively by retraining or fine-tuning models before performance drops significantly.
Implementing This Best Practice
- Track key performance metrics: Implement monitoring tools (e.g., Prometheus, Grafana) to track key metrics like accuracy, precision, recall, and error rates in production. Set thresholds to trigger alerts when performance metrics fall below acceptable levels.
- Use AI-specific monitoring tools: Tools like EvidentlyAI or Fiddler specialise in monitoring AI models and can track more detailed metrics, such as data drift, model drift, and fairness across different demographic groups.
- Automate monitoring and alerting: Automate alerts for performance degradation and integrate them with your existing incident response system (e.g., PagerDuty). This ensures that issues are detected and resolved quickly.
- Regularly review monitored data: Schedule regular reviews of monitored data to assess overall trends in model performance. Adjust your monitoring setup as needed to account for new types of data or business requirements.
Conclusion
Continuous monitoring is essential for maintaining the reliability and accuracy of AI models in production. By implementing tools that track key metrics and alert teams to performance issues, organisations can ensure their models continue delivering value and remain aligned with business objectives. Monitoring is a critical practice for long-term AI success.
