SLOs, burn rates, and AI-assisted operations
Observability & AIOps
Build an observable AI platform: GPU telemetry with DCGM and Prometheus, SLOs and multi-window burn-rate alerting, AIOps-assisted incident response, and safe automation.
What you'll learn
Module 1Module 1: Building an Observable AI Platform
- From Monitoring to Observability
- The Telemetry Signals
- RED, USE, and the Golden Signals
- OpenTelemetry and Collection Architecture
- Designing Useful Dashboards
- Knowledge Check
Module 2Module 2: SLOs and AI-System Health
- SLIs, SLOs, and Error Budgets
- Multi-Window Burn-Rate Alerting
- Model and Data Observability
- Observing LLM Applications
- Capacity, Cost, and Sustainability
- Knowledge Check
Module 3Module 3: AIOps-Assisted Incident Response
- What AIOps Can and Cannot Do
- Anomaly Detection Without Alert Storms
- Event Correlation and Topology
- AI Copilots for Operations
- Incident Workflow and Evidence
- Knowledge Check
Module 4Module 4: Safe Automation and the Incident Capstone
- Automation Maturity and Guardrails
- Policy-Based Remediation
- Governance, Security, and FinOps
- Capstone: Diagnose a Degraded LLM Service
- Operational Readiness Review
- Continuous Improvement
- Knowledge Check
Preview the first lesson
Monitoring answers questions you anticipated by checking known conditions. Observability helps you investigate conditions you did not predict by exposing enough evidence to infer a system's internal state. A dashboard saying that latency is high is monitoring; being able to connect that latency to a deployment, model version, tenant, trace, and saturated GPU is observability.
An observable AI platform has several interacting layers: infrastructure, Kubernetes, data and training pipelines, model serving, and the user-facing application. A healthy node does not prove that predictions are useful. A model can return HTTP 200 while its inputs drift, its output quality falls, or its token cost doubles. Treat technical health, model health, and product outcomes as related but distinct views.
Start every instrumentation decision with an operational question. Examples include: Are users receiving correct answers? Which dependency explains the p99 increase? Did the new model improve quality without violating the latency budget? A signal that supports no decision is usually telemetry cost without operational value.
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Hands-on terminal labs
Realistic scenario labs with a simulated cluster — no setup required.
Shareable certificate
Pass the Knowledge Checks to earn a verifiable certificate of completion.