Observabilité et Monitoring des Systèmes IA en Production

Ce qu'elle couvre

Ce programme de niveau praticien donne aux ingénieurs plateforme et aux SRE les compétences nécessaires pour instrumenter, tracer et évaluer les systèmes IA en production. Les participants apprennent à mettre en place des journaux structurés pour les appels LLM, à détecter la dérive des modèles, à configurer des alertes sur les coûts et la latence, et à exécuter des évaluations continues sur le trafic réel. Le format associe des ateliers pratiques sur des outils d'observabilité réels (LangSmith, Arize, Prometheus, OpenTelemetry) à des revues d'architecture et des post-mortems d'incidents. À l'issue du programme, les équipes sont capables de construire et d'exploiter une stack d'observabilité IA de niveau production.

À l'issue, vous saurez

• Instrument an LLM API pipeline with OpenTelemetry spans and structured JSON logs exportable to any backend
• Configure automated eval pipelines that score model outputs on live traffic using LLM-as-judge and rule-based checks
• Build a drift detection alert that triggers when embedding cosine similarity or output toxicity scores shift beyond a set threshold
• Define and implement SLOs for AI endpoints covering latency P95, error rate, and per-token cost budgets
• Diagnose and remediate a simulated production incident involving a degraded LLM response quality using an observability dashboard

Sujets abordés

• Tracing LLM calls with OpenTelemetry and vendor-specific SDKs
• Structured logging strategies for prompt/response pipelines
• Eval-in-production: running automated quality checks against live traffic
• Drift detection for embedding models and output distributions
• Cost monitoring and token-budget alerting across providers
• Latency profiling and SLO definition for AI endpoints
• Observability tool landscape: LangSmith, Arize, Helicone, Datadog AI
• Incident response playbooks for degraded model behaviour

Modalité

Delivered as a 3-day intensive bootcamp (in-person or remote with live instruction), with approximately 60% hands-on lab time and 40% concept delivery and discussion. Participants work in small squads on a shared staging environment pre-configured with a multi-step LLM application. Materials include lab notebooks, architecture reference cards, and a starter observability stack template (Docker Compose + Prometheus + Grafana + LangSmith). Remote delivery uses breakout rooms for squad work with a shared cloud sandbox. Post-bootcamp, a follow-up office-hours session (2h) is recommended at the 30-day mark.

Ce qui fait que ça marche

• Defining SLOs for AI endpoints before instrumenting — clarity on what 'good' looks like drives the right metric selection
• Embedding eval-in-production as a standard release gate alongside unit and integration tests
• Assigning clear ownership of cost and quality dashboards to a named team or on-call rotation
• Starting with a minimal viable observability stack (traces + cost + one quality metric) and iterating rather than boiling the ocean

Erreurs fréquentes

• Treating LLM observability as identical to classic APM — ignoring semantic drift and output quality as first-class signals
• Logging only errors and latency while omitting prompt content and token counts, making cost attribution impossible
• Running evals only offline at release time and having no mechanism to catch quality regressions in live traffic
• Setting up dashboards without defining SLOs first, resulting in metrics that nobody acts on

Fournisseurs à considérer

• Arize AI (training resources & documentation)arize.com/blog-course/ →
• DataScientestdatascientest.com →
• Maven (AI Engineering cohorts)maven.com →
• DeepLearning.AI (short courses on LLM evaluation)www.deeplearning.ai/short-courses/ →

Sources

• OpenTelemetry for LLM Applications — OpenTelemetry Blog →
• LLM Evaluation in Production — Arize AI Documentation →