Continuous Performance Monitoring
Model quality is not static. As user behaviour evolves, deployment contexts shift, and models are updated, the performance characteristics that passed pre-deployment evaluation can degrade in ways that automated monitoring does not catch. É«µ¼º½'s continuous performance monitoring service provides the ongoing human evaluation layer that detects model drift, capability regression, and emerging failure modes before they affect users at scale.
What É«µ¼º½ Delivers
Regular Evaluation Cadence
Drift Detection Evaluation
Emerging Failure Mode Identification
Performance Reporting and Alerting
Human Monitoring and Automated Monitoring Together
Automated monitoring catches volume-detectable patterns: high refusal rates, latency spikes, and format failures. Human monitoring catches quality degradation: subtly worse responses, increased hallucination rates, and emerging bias patterns that do not produce anomalous system metrics but do produce worse user experiences.
A/B testing and hallucination benchmarking provide the diagnostic depth when monitoring identifies a problem. Continuous monitoring is the early-warning system that tells you a problem exists.
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