Tag
#drift-detection
5 posts tagged drift-detection.
- monitoring
Model Monitoring in Production: A Four-Layer Framework
Model monitoring covers more than drift detection. Here's the four-layer framework — software health, data quality, model quality, business KPIs — wired up with Evidently, PSI thresholds, and real alert logic.
- monitoring
Model Monitoring for LLM Inference: Metrics Your APM Can't See
Model monitoring for LLM APIs requires a different metric set than traditional ML. Here's the signal hierarchy — TTFT, KV cache hit rate, output length drift, refusal rate — wired up with OpenTelemetry and Prometheus.
- mlops
ML Testing: A Checklist from Pre-Train Checks to Production Drift
ML testing spans pre-train sanity checks, behavioral validation, data integrity, and continuous drift monitoring. Here's what actually belongs in your CI pipeline and runbook.
- deep-dive
When Embedding-Based Defenses Fail in Multi-Agent LLMs
A new arXiv paper shows that embedding-distance detectors miss three classes of adversarial agent. The fix lives in your observability stack, not your prompt template.
- tooling
Model Monitoring Tools: A Technical Comparison for ML Teams
Evidently, Arize, WhyLabs, Fiddler, NannyML, Alibi Detect — how each tool actually detects drift, what it costs to run, and which one fits your stack.