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Data Quality for ML (Validation, Drift & Monitoring Basics)
Make ML reliable by improving data quality: validation rules, training/serving parity, drift signals, and monitoring foundations.
Data QualityMonitoringDriftMLOps
Duration
2 days
Format
Remote
Level
Beginner
Key outcomes
- Define quality rules and validation checks
- Detect anomalies and drift signals (foundations)
- Ensure training/serving parity for features
- Create a minimal monitoring plan (KPIs + alerts)
Program
Detailed syllabus will be published soon. Contact us to receive the latest version.