21 June 2020
8 min
Data Quality for ML: Validation, Drift, and Parity in Practice
If data changes silently, models fail silently. Quality is an engineering system.
Data QualityMLOpsMonitoring
If data changes silently, models fail silently. Quality is an engineering system.
Framework
- Schema checks + constraints
- Parity checks (training vs serving features)
- Drift checks (distribution shift)
- Alerting thresholds + investigation workflow
Pitfalls
- Only checking missing values (ignoring unit/time issues)
- No parity tests between training and production pipelines
- No label pipeline → cannot measure real performance
Portfolio deliverables
- Validation suite + CI gate
- Drift dashboard + alert thresholds
- Parity checklist (features available at inference time)
Good practice
Ship a baseline + monitoring first. Then iterate with evidence.
FAQ
Can drift be normal?
Yes. Monitor it to decide if retraining is needed or if the process changed.
What’s the fastest tool to start?
Start with simple checks + dashboards. Tools help later.
Want to go deeper?
Ask for a brochure, a syllabus, or a live walkthrough of our training projects and delivery standards.
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