Productionizing AI Systems
The FDE's other half of the job: take an AI prototype that earned a 'go' and make it survive production — keep working, safely, cheaply, unattended, as the world changes. Learn the four shared pillars (evaluate, ship, run, govern), then productionize each system type — ML (drift & retraining), RAG (retrieval evals & grounding), and agents (approval gates & guardrails) — and scale and hand off to a delivery team. The back half of the FDE journey; brings together evaluation, CI/CD, monitoring, and guardrails.
"The prototype earned a go — now take it to production without it becoming a 3am incident"
9 Interactive Sessions
Short, interactive sessions — watch it work, steer it, then build it yourself. Go deeper anytime with the full code walkthrough.
- 1
Shipping the prototype is the disaster
See why a prototype and a production system answer different questions — and why the move after a 'go' is a pilot, not a launch.
- 2
You can't productionize what you can't measure
Build the eval that tells you the system still works — and would catch it breaking — and make it the gate every change must pass.
- 3
Ship a change without holding your breath
Deploy AI changes safely — versioning every artifact, gating CI on the eval, rolling out by canary, and keeping rollback one command away.
- 4
It runs unattended — so it has to watch itself
Watch the signals that reveal an AI system silently failing — quality, cost, latency, safety, override rate — and alert on the ones that matter, recall-first.
- 5
The model is right today and slowly wrong after
Take an ML model to production against its killer failure — silent accuracy decay from drift — with detection, retraining, and confidence-gated human review.
- 6
The confident wrong answer
Take a RAG system to production against its killer failure — a confident, fluent, wrong answer from stale or irrelevant retrieval — with retrieval evals, index freshness, and grounding guardrails.
- 7
When the system can act, guardrails are architecture
Take an agent to production against its killer failure — a wrong, irreversible action — by making guardrails part of the architecture, not a warning in the prompt.
- 8
It works. It's just too expensive to run.
Optimize and monitor the three resources AI burns — tokens, compute (CPU/GPU), and memory — so the system is sustainable at scale, not a loss-leader.
- 9
A system only you can run is a dependency
Scale the system to more load and more users, then hand it off so a team that didn't build it can run it without you.
Production patterns you'll master
Synthetic data included
- The three systems (ML/RAG/agentic)
- Production-readiness checklist
- Evaluation primer
- Drift & monitoring scenarios
- Handoff runbook template
What you walk away with
Shareable portfolio
A public URL showing your module timeline, patterns mastered, and completion status.
All the code
Download everything as a ZIP — pipelines, guardrails, deployment configs. Yours forever.
Module walkthrough
Each module documented with deliverables and the production pattern you implemented.
Ready to build your productionizing ai systems?
First course free. $20 per course after that.