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Postmortem

As explained in Machine Learning System Design

A postmortem is a structured analysis of an incident and its causes, effects, and follow-up actions.

A postmortem is a structured way to learn from production failures rather than repeat them. Pair incident resolution with proportional follow-up: match the response to the failure’s effect and the chance of recurrence. section 16.1

A practical postmortem can lead to:

  • A short discussion and a runbook update for a limited incident. section 16.1
  • A broader reliability initiative when an outage exposes systemic risk. section 16.1

Proportional response

A small failure may merit ten minutes of discussion and one paragraph in the runbook; a major outage may justify coordinated action across engineering teams. section 16.1

How Machine Learning System Design covers it

How deep it goes
Defines retrospectives, postmortems, and proportional response, but stops short of prescribing a detailed investigation template.
Examples
Uses minor incidents requiring runbook updates and a major outage prompting a company-wide reliability initiative.
What it assumes
Assumes familiarity with production incidents, on-call responsibilities, and operational runbooks.
What you can do after
Practice proportional postincident learning and convert failures into appropriately sized follow-up actions.
How it connects
Connects incident response with retrospectives, runbooks, on-call operations, technical debt, and reliability.
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