Learning objectives
- • Write data checks that catch schema, distribution, and freshness failures
- • Separate hard failures from warn-only checks
- • Connect data contracts to training pipelines and serving paths
MLOps
Data Validation and Data Quality
Add production data quality coverage for schemas, ranges, missingness, freshness, outliers, and contract testing.
Recommended on day 3185 minutesIntermediate
Interview prompts
- • What data checks would block a retraining job?
- • How do you detect a silent upstream instrumentation change?
Prerequisites