Course Overview: What You’ll Learn in AI Log Aggregation and Structured Logging

COURSE OVERVIEW

Welcome to AI Log Aggregation and Structured Logging

A practical, AI-forward course built for professionals shipping real work in 2026.

Design a structured log schema that survives three years of product drift
Emit JSON logs from Node, Python, and Go without a homegrown logger
Route logs through the modern pipeline (app -> agent -> aggregator -> cold storage)
Use AI to cluster errors, group by root cause, and surface novel anomalies
Write log-driven runbooks so the oncall can resolve incidents from the dashboard

LESSON 2

Why Unstructured Logs Are a Tax You Pay Forever

The case for structured logs, the specific cost of unstructured ones, and the four-line test that tells you which camp you're in.

LESSON 3

The Log Schema: Required Fields, Optional Fields, and the 'Do Not Log' List

Design a logging schema that survives product drift and audit review — the nine required fields, the five optional ones, and the PII landmines.

LESSON 4

Emitting JSON Logs From Node, Python, and Go

Drop-in structured loggers: pino for Node, structlog for Python, zerolog for Go — and how to wire them to stdout for the agent to scoop up.

LESSON 5

The Pipeline: App -> Agent -> Aggregator -> Cold Storage

Route logs through the four-stage pipeline without losing events at the edges — agents, buffers, retention tiers.

LESSON 6

AI for Error Clustering and Root-Cause Grouping

Use LLMs to collapse 10,000 stack traces into the 12 underlying root causes — and the prompting pattern that doesn't make up errors.

LESSON 7

Log-Driven Runbooks and the 90-Second Incident Response

Turn logs into action: runbooks that tell the oncall what to do in the first 90 seconds, driven by log queries, not vibes.

LESSON 8

Retention, Cost, and the Logs You Should Not Keep

Design a retention policy that keeps audit-relevant logs for years without paying Elastic prices for debug noise.

Backend and platform engineers who own production services
SREs and devops engineers building or refactoring observability
Founders running a small oncall where every hour of debugging hurts
Engineering leaders deciding between Datadog, Grafana Loki, Elastic, or homegrown

Vendor-agnostic — the patterns outlast whatever tool you buy
AI is used for the specific jobs it's good at: clustering, summarization, anomaly flags
Every lesson ends with shippable code or config, not just theory
Built for 2026 scale (100k-10M events/day), not FAANG-volume

8 lessons. Hands-on. Evergreen. Built to ship.

$9.99

One-time purchase. Lifetime access.

Vibe Coding People — hands-on AI skills for builders, designers, and marketers.