Productionizing AI Systems
Take an AI prototype that earned a 'go' and make it survive production — safely, cheaply, unattended, as the world changes. The four shared pillars, then productionizing ML, RAG, and agents, then scale and handoff.
Modules
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Verified
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In Progress
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What you walk away with
- ✓A shareable portfolio URL with your project walkthrough
- ✓Module-by-module timeline of everything you built
- ✓All the code — pipelines, guardrails, deployment configs
- ✓Production patterns documented on your profile
A prototype answers 'does it work once?'; production answers 'does it keep working — safely, cheaply, unattended — as the world changes?' This is the back half of the FDE journey. You pilot on real live data, then build the four pillars every production AI system needs — evaluation, CI/CD, observability, guardrails — and productionize each system type on its own sharp edges: ML (silent drift → detection + retraining), RAG (confident wrong answers → retrieval evals + grounding), and agents (unsafe actions → approval gates + tool sandboxing + cost caps). Finally you scale the load and hand the system off to a delivery team with runbooks, ownership, and SLAs. Brings together evaluation, CI/CD, monitoring, and governance.