Productionizing AI Systems

9 Interactive Sessions

Short, interactive sessions: watch it work, try it, then build it yourself. You can go deeper at any time in the written chapters.

1. 1

   From a working demo to a system people rely on

   A prototype shows an idea works once. A production system has to keep working, every day, with nobody watching. The step after a “go” is a pilot, not a launch.

2. 2

   Measure it, so you know it still works

   Build an eval that tells you the system still works and would catch it breaking, and make it the gate every change must pass.

3. 3

   Ship a change you can undo

   Release AI changes safely: keep a version of every part that changes behaviour, let the eval decide what ships, release to a small slice first, and keep the way back one command away.

4. 4

   Watch the signals that uptime cannot see

   Watch the signals that show an AI system quietly failing (quality, corrections by people, cost, speed and safety) and send an alert on the ones that matter, putting safety first.

5. 5

   Keep a model accurate as the world changes

   Take an ML model to production against its own failure, accuracy that quietly drops as the world changes, with checks per category, retraining and a person for unsure cases.

6. 6

   Answer from the documents, or say you can't

   Take a RAG system to production against its own failure, a confident, well-written, wrong answer, by checking the search, keeping the index fresh, and citing or refusing.

7. 7

   Give an agent hard limits, written in code

   Take an agent to production against its own failure, a wrong action that cannot be undone, by building the limits into the code instead of asking for care in the prompt.

8. 8

   Make it cheap enough to run every day

   Measure and cut the three resources AI uses (tokens, computing power and memory) so the system pays its way at scale.

9. 9

   Hand it over so it runs without you

   Scale the system to more traffic and more users, then hand it to a team that did not build it, so it keeps running without you.

How this certificate is scored

Your certificate shows how many of your written answers met the rubric below, checked by an AI grader. Anyone who checks your certificate sees it.

• You answer 9 short questions, one for each module, in your own words (at least 40 words each).
• An AI grader checks each answer against what its module asks for: it must answer the question, use the course's practice case specifically, and be correct.
• To pass, at least 8 of the 9 answers must meet that bar. The certificate says the answers were graded by AI.

The rubric

1. Module 1, From Prototype to Production: A pilot plan + production-readiness assessment for one system.
2. Module 2, Evaluation: An eval suite + a pass/fail quality gate.
3. Module 3, Ship It (CI/CD): An eval-gated deploy pipeline with a rollback plan.
4. Module 4, Run It (Monitoring): A monitoring dashboard spec + the top alerts for one system.
5. Module 5, Productionizing ML: A drift-monitored, retrainable ML service plan.
6. Module 6, Productionizing RAG: A RAG system with retrieval evals, a reindex plan, and grounding guardrails.
7. Module 7, Productionizing Agentic Solutions: An agent with approval gates, tool guardrails, cost caps, and a trace-based eval.
8. Module 8, Efficiency — Token, Compute & Memory: A resource-optimization + monitoring plan across tokens, compute, and memory.
9. Module 9, Scale & Handoff: A scale plan + a handoff package (runbook, ownership, SLAs).

What you walk away with