Intermediate|10 hours|18 lessons

Observability Fundamentals for Engineers

A free course covering the observability knowledge that separates engineers who debug incidents in minutes from those who stare at Grafana for hours. The three pillars, PromQL, OpenTelemetry, SLOs with error budgets, and the dashboard/alert patterns that actually work in production.

Completely Free

No signup required. Start learning now.

Text-based, no videos
6 modules, 18 lessons
No signup required

What you'll learn

Monitoring vs observability — and why the distinction matters for distributed systems
The Four Golden Signals (latency, traffic, errors, saturation) applied to real services
Cardinality — why high-cardinality labels explode metrics costs and how to bound them
Prometheus data model, counters/gauges/histograms, and PromQL you will actually use
Structured logging, correlation IDs, log levels, and sampling strategies for cost control
Distributed tracing with OpenTelemetry — spans, context propagation, head vs tail sampling
SLIs, SLOs, and error budgets — setting realistic targets and knowing when to stop shipping
Grafana dashboards that help during incidents (not 50-panel noise)
Alerting on symptoms not causes, burn-rate alerts, and cutting alert fatigue
The metrics → logs → traces debugging flow for real production incidents

Curriculum

6 modules · 18 lessons
01

What Observability Actually Is

The mental model that separates monitoring from observability. The three pillars, the four golden signals, and why cardinality is the hidden cost center.

3 lessons
Monitoring vs Observability30 minFREEThe Four Golden Signals30 minFREECardinality and Why It Matters25 minFREE
02

Metrics with Prometheus

The Prometheus data model, PromQL queries you will actually write in production, and how to design metrics that help rather than blow up your costs.

3 lessons
The Prometheus Data Model30 minFREEPromQL Fundamentals35 minFREEWriting Good Metrics30 minFREE
03

Logs That Actually Help

Structured logging, log levels that engineers understand, and cost-aware aggregation — the difference between logs that solve incidents and logs that cost a fortune for nothing.

3 lessons
Structured Logging30 minFREELog Levels and What They Mean25 minFREELog Aggregation and Cost30 minFREE
04

Distributed Tracing

Spans, context propagation, OpenTelemetry, and sampling — the missing piece when an incident spans multiple services.

3 lessons
What Tracing Is30 minFREEOpenTelemetry Fundamentals30 minFREETracing in Practice30 minFREE
05

SLIs, SLOs, and Error Budgets

Service level indicators and objectives, error budgets, and the decision framework that separates reliability-aware teams from checkbox-compliant ones.

3 lessons
SLI: What You Measure30 minFREESLO: What You Commit To30 minFREEError Budgets and Decision Making30 minFREE
06

Observability in Practice

The dashboard, alerting, and debugging patterns that turn the three pillars into fast incident resolution.

3 lessons
Grafana Dashboards That Do Not Suck30 minFREEAlert Design30 minFREEDebugging with Observability35 minFREE

About the Author

Sharon Sahadevan

Sharon Sahadevan

AI Infrastructure Engineer

Building production GPU clusters on Kubernetes — H100s, large-scale model serving, and end-to-end ML infrastructure across Azure and AWS.

10+ years designing cloud-native platforms with deep expertise in Kubernetes orchestration, GitOps (Argo CD), Terraform, and MLOps pipelines for LLM deployment.

Author of KubeNatives, a weekly newsletter read by 3,000+ DevOps and ML engineers for production insights on K8s internals, GPU scheduling, and model-serving patterns.

Start learning now — completely free

6 modules, 18 lessons. No signup, no paywall.