A notebook that works once is a proof of concept. Shipping five production AI apps in six months taught me the same checklist applies whether the app is a RAG platform or a fine-tuning dashboard.

The non-negotiables

  1. Containerise it. Docker means the same image runs in dev and in production, no "works on my machine."
  2. Structured logs from day one. JSON logs you can query beat print statements you have to grep.
  3. Metrics before you need them. You want to know something's wrong from a dashboard, not from a user's complaint.
  4. A test suite that runs in CI. 107 passing tests on FineTune Studio, 260+ on FaceVision — not because testing is fashionable, but because it's the only thing that lets you change code without dread.
  5. A fallback for every external call. Every LLM, embedding and reranking provider in RAG.NextUpgrad has a second option that kicks in automatically on failure.

The part nobody puts in the tutorial

Health checks. `/healthz` says the process is up. `/readyz` says the thing the process depends on — a vector store, a database — is actually reachable. The two are different questions, and conflating them means your monitoring lies to you exactly when you need it most.