Data Drift

Monitoring vs. Observability in MLOps: A Three-Pillar Framework for Keeping Production Models Healthy

Model monitoring and observability are distinct practices crucial for maintaining trustworthy machine learning systems post-deployment. A practical MLOps strategy employs a three-pillar framework—data integrity and drift, model performance, and operational health—to diagnose and resolve issues.