DevOps
Intermediate
AI Application Observability
Instrument AI application behavior so reliability issues become diagnosable.
Course Overview
Define useful request metrics, capture safe operational context, and connect failures to user-visible outcomes. The course treats logs and traces as product infrastructure, not an afterthought.
What You Will Learn
Instrument AI request telemetry: latency, token usage, cost, and error metrics
Implement distributed context propagation across LLM calls and tool executions
Set up LangSmith/OpenTelemetry tracing without locking into proprietary vendors
Detect latency spikes, context window overflow, and rate-limit bottlenecks
Establish alert thresholds for model regression and API budget bounds
Tools & Technologies Used
OpenTelemetry
LangSmith
Python
FastAPI
Prometheus
Structured Curriculum
2 Modules · 6 Lessons · 150 Minutes Total
Module 1
Module 1: Telemetry & Tracing Foundations
3 lessons
Module 1: Telemetry & Tracing Foundations
Capture logs, metrics, and execution spans for model calls.
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Metrics That Matter in AI Applications
Track time-to-first-token, total latency, token consumption, and failure rates.
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Distributed Tracing with OpenTelemetry Spans
Create nested span contexts around retrieval, model generation, and tool execution steps.
-
LangSmith Tracing Integration
Configure non-blocking trace exporters with zero application downtime fallback.
Module 2
Module 2: Diagnostics, Cost & Reliability Monitoring
3 lessons
Module 2: Diagnostics, Cost & Reliability Monitoring
Build operational dashboards, cost trackers, and failure alerts.
-
Cost Tracking & Token Budget Governance
Calculate real-time cost attribution per user session and trigger budget caps.
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Diagnosing Latency & Timeout Failure Modes
Use trace visualizers to pinpoint upstream API delays and slow retrieval queries.
-
Automated Reliability Guardrails & Alerting
Set up automated alerts for elevated error rates and model degradation.
Practical Project & Capstone Outcome
🚀 Capstone Project
AI Service Observability Dashboard & Exporter
Instrument a multi-step RAG FastAPI application with OpenTelemetry spans, token usage counters, and an admin diagnostics endpoint.
Prerequisites
- Basic understanding of web backend architectures and logging
Intended Audience
- DevOps Engineers and Site Reliability Engineers monitoring AI services
- Backend Engineers building production AI pipelines
Instructor Information
P
Priya Nair
Course Author & Industry Expert
Priya Nair is a Principal SRE with 12 years of experience managing infrastructure observability for high-scale microservices.
Frequently Asked Questions
Does this require paid observability software?
No. The course uses open-source OpenTelemetry standards compatible with free local tools.