Phase 8: Production Hardening & Operations · 55 min · Python · Docker · Kubernetes

The PoC-to-Production Gap

A demo on 10 examples is not a system serving 10,000 users.

What you will learn

Identify scalability gaps: single-user demo to concurrent users, connection pooling, queue-based processing
Identify security gaps: hardcoded keys to secret management, open endpoints to authenticated access
Identify reliability gaps: no error handling to retries/fallbacks, no monitoring to alerting
Identify compliance gaps: dev data to real PII, no access control to RBAC, no data retention policy
Apply the 40+ item hardening checklist across security, reliability, performance, compliance, observability

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Identify scalability gaps: single-user demo to concurrent users, connection pooling, queue-based processing
Identify security gaps: hardcoded keys to secret management, open endpoints to authenticated access
Identify reliability gaps: no error handling to retries/fallbacks, no monitoring to alerting
Identify compliance gaps: dev data to real PII, no access control to RBAC, no data retention policy
Apply the 40+ item hardening checklist across security, reliability, performance, compliance, observability

The Problem

A demo that works on 10 examples is not a system serving 10,000 users. The gap between proof-of-concept and production is where most AI projects die — and it's the gap FDEs are hired to close. This lesson covers the specific differences: error handling, rate limiting, caching, observability, access control, and the 40+ items on the production hardening checklist.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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