Machine learning models are unlike traditional software in one crucial way: their accuracy depends on real-world data that keeps changing after deployment. Customer behavior shifts, upstream data sources change schema without notice, and the world the model was trained on gradually diverges from the world it now operates in—a phenomenon known as drift.
Treating models as living systems
Mature MLOps practice monitors prediction quality continuously, not just infrastructure uptime, comparing live outcomes against ground truth as it becomes available and alerting when performance degrades beyond an agreed threshold. Retraining pipelines should be automated and version-controlled with the same rigor as application code, including rollback capability when a new model version underperforms the one it replaced.
Feature pipelines deserve the same reliability engineering as the model itself, since a broken upstream data feed silently degrades predictions long before anyone notices the model is technically still running.
JIG helps enterprises operationalize AI with MLOps practices that catch degradation early, keeping production models accountable long after the initial launch celebration.