monitoring-observability

Detect data drift in LLM outputs using statistical tests like Population Stability Index (PSI) and Kolmogorov-Smirnov (KS) tests, which identify significant shifts in input data distributions and output quality based on historical baselines.

Implement Prometheus metrics for FastAPI using the RED method to track Rate, Errors, and Duration, instrumenting latency and error rates while managing metric cardinality to prevent database overload.

Yes, you can integrate Langfuse for LLM observability to implement tracing for LLM calls, track token costs, set up evaluation scoring, and monitor drift in your application's LLM outputs.

Statistical methods for drift detection in machine learning include Population Stability Index (PSI) and Kolmogorov-Smirnov (KS) tests, which analyze data distributions to detect shifts in model inputs and outputs.

Create Grafana dashboards and alerting rules by configuring visualization panels for Prometheus metrics and setting threshold-based alerting strategies aligned with Service Level Objectives (SLOs) for application health.

Best practices for managing Prometheus metric cardinality involve limiting high-dimensional label combinations and avoiding user IDs or request IDs as labels to prevent database bloat and maintain query performance.