Deployment and MLOps · MLD-0760
Model monitoring
Tracking a deployed system's inputs, outputs, performance, and operational behavior.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Watch latency, failures, drift, and sampled answer quality.
Original caveat
Performance monitoring may require delayed or unavailable labels.
Context and aliases
Level: Intermediate. Stages: Deployment · Monitoring.
Tags: ML · DL · LLM · SLM · MLOps.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- Arize Phoenix — Open-source AI observability and evaluation tooling.
- Docker — Container packaging and runtime tooling.
- Evidently — Data and ML monitoring/evaluation library.
- Grafana — Dashboarding and observability application.
- Kubernetes — Container scheduling and orchestration platform.
- Langfuse — LLM application tracing, prompt and evaluation platform.
- LangSmith — Observability, evaluation and development platform for LLM applications.
- OpenTelemetry — Vendor-neutral APIs and conventions for telemetry.
- Prometheus — Metrics collection and alerting system.
- whylogs — Data profiling library using compact statistical summaries.
Original references
- Transformers glossary ↗ · Consulted
- Google Cloud MLOps architecture ↗ · Reference link
- scikit-learn User Guide ↗ · Consulted
- Original reference URL ↗