04
Observable
Can you detect problems before they affect AI systems?
The Shift
AI operates at machine speed. By the time you notice something's wrong, thousands of decisions have been made. Problems must be detected upstream, not discovered downstream.
Requirements
What must be true about the data itself.
- Data quality validated before AI consumption
- Anomalies detected proactively
- AI outputs traceable to input data
Capabilities
What your infrastructure must support.
Automated data quality testing
Validate completeness, correctness, validity in pipelines
Anomaly detection
Identify unexpected values, volume shifts, distribution changes
Schema drift detection
Alert when schemas change unexpectedly
Pipeline observability
Monitor DAG execution, failures, and latency
Input-output tracing
Link AI decisions to the specific data versions that informed them