Capstone · 8–10 hrs

Week 26 — Capstone IV — Ship, Measure, Defend

Capstone live with pilot users, measured, defended, and written up publicly

Mock review
staged rolloutpilot usersexperiment readoutrunbooklaunch memocapstone defenceretrospectivepublishing

Ways of working: Course retrospective

Capstone · Mock review #7: the final panel

1. Ship (~2.5 hrs)

  • Production environment separate from dev and preview, deployed from CI only
  • Staged rollout: you → 2–3 friendly users → full pilot group, watching SLOs and errors at each step
  • Runbook covering the top 5 failure modes, kill switch, rollback and contacts
  • The experiment from week 25 running

2. Measure (~2 hrs)

At the end of the week (or as late as the sample allows):

  • Experiment readout with a confidence interval and a decision (ship, kill or iterate)
  • North-star and guardrail metrics against the targets set in week 23
  • AI-feature production quality: a sample of real outputs graded against your eval rubric. Did production match the offline evals?
  • Actual cost vs the estimate

3. Launch memo (~1 hr)

Write one page for an exec audience covering:

  • what you built and for whom
  • the result, with numbers and uncertainty
  • the risks you’ve handled and the ones that remain
  • what it would cost to scale
  • the recommendation and the next three bets

No jargon.

4. Capstone defence (~1.5 hrs)

Tell Claude: “Run the final panel: a staff SWE, a senior data engineer, an SRE and an exec. 60 minutes. Don’t go easy.” Present:

  • the design doc
  • a demo
  • eval results
  • the experiment readout
  • the cost
  • the postmortem from any real incident

Pass bar:

  • You defend each major decision with its trade-offs.
  • You identify your system’s weakest point before the panel does.
  • You say “I don’t know, here’s how I’d find out” where that’s the honest answer.
  • The exec understands the memo without asking for translation.

5. Retrospective (~1 hr)

Write retro.md:

  • your week 1 vs week 26 self-assessment on CLI, git, Python, TypeScript, SQL, Databricks, AI engineering, production, system design and stats (1–5 each)
  • where estimates were most wrong, across the whole course
  • what you’d now do differently as a PM, and what you’ll stop doing
  • the next 3 skills to deepen

6. Publish (~1 hr)

  • Clean up the repo: README, architecture diagram, how to run it, and screenshots.
  • Turn your 26 teach-backs into an index page.
  • Write a public post: “26 weeks from PM to product engineer: what I built and what I learned.”

Done when

  • Capstone live with pilot users
  • Experiment readout and metrics reviewed
  • Launch memo written
  • Final panel passed
  • Retrospective written
  • Repo and teach-backs published