Mind Your Models: 5 Ways to Implement ML Monitoring in Production

Machine learning (ML) models are crucial for modern businesses, but monitoring their performance is essential to ensure they maintain expected outcomes. ML monitoring involves tracking the model's performance metrics, data quality, and overall application health. This article discusses five ways to implement ML monitoring in production: detecting data drift, monitoring models for concept drift and performance, checking ML pipelines for data quality, monitoring AI explainability, and ensuring bias and fairness. By implementing a robust monitoring system, businesses can optimize their operations, reduce costs, and mitigate risks, ultimately leading to better outcomes for both businesses and customers.