Engineering-focused coverage of ML observability and MLOps. Model monitoring, drift detection, training/serving skew, debugging production model failures, evaluation pipelines, and the tooling that actually works at scale.
Mapping LLM application telemetry to MITRE ATLAS techniques. Concrete log shapes, alerting heuristics, and a runbook structure that scales beyond ad-hoc grep rules.
A new arXiv paper certifies controllability and ISS robustness for an LLM-driven SOC agent using Lean 4. The MLOps takeaway is simpler than the math: monitor the action catalog, not the model.
Orchid Security's framing of agent governance as a delegation problem lands in the lap of ML observability teams. The instrumentation we already own decides whether the authority graph is real or theatre.
A new paper demonstrates three attack patterns — Slow Drift, Benign Wrapper, Chaos Seeding — that defeat embedding-based detection of malicious agents in LLM multi-agent systems. The fix requires monitoring logit-level confidence, not just output embeddings.
A practitioner's Reddit report on running Qwen3.6-27B locally signals a real inflection point. But moving off managed cloud APIs shifts monitoring responsibilities squarely onto your own infra.
A new framing of AI agent risk argues that delegation, not identity, is the missing telemetry. ML platform teams already have the substrate to fix it.
Security vendors are pitching 'continuous observability' as the answer to ungoverned AI agents. ML platform teams already shipped most of the pipes. The missing piece is identity context inside the trace span — and that is a schema fight, not a tooling fight.
SentryML covers ML observability and MLOps from a production-engineering perspective. Here's what we publish.
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