Results for “model-observability”

Use this when the system needs logging, metrics, tracing, alerting, or operator-visible runtime signals for APIs, workers, integrations, serverless functions, or local-to-production supportability.

Projeta métricas, logs, traces, dashboards, alertas e SLO com Prometheus, Grafana, Loki, Tempo, Mimir, Zabbix e Alertmanager, incluindo consultas PromQL e boas práticas de alertas.

Self-hosted observability platform for LLM applications, providing tracing, evaluation, datasets, experiments, and real-time monitoring to debug and improve AI systems.

Trace, evaluate, and monitor LLM applications with an open-source observability platform.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Implements a complete observability stack with Prometheus metrics, Grafana dashboards, Jaeger distributed tracing, and structured logging, including heartbeat-based absence detection for scheduled jobs on Google Cloud.

Adds logging, metrics, tracing, and alerting to make production behavior visible and diagnosable.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Debug, evaluate, and monitor LLM applications with tracing, datasets, and built-in evaluators.

Design production-ready observability strategies combining metrics, logs, and traces, including SLI/SLO design, golden-signals monitoring, and alert optimization.

Design and apply QA methodology for software teams: test strategy, regression testing, CI failure triage, test automation, quality gates and metrics, risk-based testing, exploratory testing, test design techniques, AI code quality gates (independent verification, acceptance-criteria testability review for agentic Spec-Driven Development), mutation-guided test hardening and review evidence (surviving mutants, weak assertions, diff-aware mutation testing), agentic eval design (dataset test design, judge-as-system-under-test, flaky-eval discipline), QA career levels (Senior/Staff/Principal), and SDET engineering (test infrastructure, gTAA, CI/CD integration). Do not use for root-cause debugging of production incidents, security implementation or threat modeling, or evaluation framework governance and statistical analysis — route those to systematic-debugging, secure-software-engineering, and agent-evals-and-observability respectively.