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Observability
ML observability means:
- monitoring model behavior
- understanding WHY the model behaves that way
- detecting issues early
- supporting debugging and retraining decisions
ML Observability Pillars
- Data Quality Monitoring
- Drift Monitoring
- Operational / System Monitoring
- Explainability & Bias Monitoring
- Governance, Lineage & Reproducibility
Data Quality Monitoring
Tracks whether the input data is valid, clean, and reliable.
- missing values
- invalid values
- type issues
- schema changes
- outliers
- range violations
- feature null spikes
Operational / System Monitoring
- throughput
- hardware utilization
- inference failures
- API timeouts
- memory leaks
- GPU/CPU load spikes
- queue lag in streaming pipelines
This ensures the model endpoint or batch job is healthy.
Governance, Lineage & Reproducibility
Tracks the lifecycle and accountability of all ML assets.
- dataset versioning
- model versioning
- feature lineage
- pipeline lineage
- audit logs (who deployed, who retrained)
- model approval workflow
- reproducible experiments
- rollback support