Title: Discussion 15 — How Much Automation and Human Review Does a Pipeline Actually Need?
Course: AIINFRA 101: Applied Infrastructure & Containerization > Section: Week 15 — Automated Model Deployment, Monitoring & Cost Optimization

💬 Discussion 15 — How Much Automation and Human Review Does a Pipeline Actually Need? (20 pts)

Across this course you've seen every piece of a production ML system: containerized services, CI/CD pipelines, blue-green and canary rollouts, monitoring, and cost control. Fully automated pipelines are repeatable and fast, but they can also silently ship regressions or quietly waste money if nobody is watching the right signals. Post your answer to this question: For a model service you'd actually want to run in production, where would you insist on a human checkpoint — deployment approval, a canary hold period, a cost/rightsizing review — and where would you trust automation alone? Ground your answer in something concrete: the stakes of a specific model (e.g., a recommendation model vs. a fraud-detection model), a team size, or a cost/risk tradeoff from this week's material. Respond to at least two classmates, ideally engaging with someone who drew the line in a different place than you did.