Pick a depth. Each prompt opens in your AI pre-loaded with the lesson. Click a row to preview the prompt.
Teams don't fail at MLOps because they pick the wrong tool — they fail because they try to install level-2 automation on a level-0 process and it collapses under its own weight. The maturity model (manual process → ML pipeline automation → full CI/CD automation) gives you a map: it tells you what 'good' looks like one step up from where you are, and stops you from over-engineering. Knowing your current level is the single most useful diagnostic in this course, because it tells you which of the next nine modules you actually need first. The honest answer for most teams in 2026 is 'level 0 with a notebook and a manual deploy,' and that's fine as a starting point as long as you know it.
The demo encodes the three levels as a self-assessment: you answer a few yes/no questions about your process and it tells you your level and the single most valuable next step — not a wholesale rewrite.
def maturity_level(automated_training, automated_pipeline, automated_cicd):
if automated_cicd and automated_pipeline:
return (2, "Full CI/CD: code+pipeline changes auto-tested and deployed. Focus: governance, drift, cost.")
if automated_pipeline:
return (1, "Pipeline automation: retraining runs automatically. Next: add CI/CD for the pipeline code itself.")
return (0, "Manual: notebooks + hand deploys. Next: get experiment tracking + versioned data BEFORE automating anything.")
# Honest self-assessment for a typical team:
level, advice = maturity_level(automated_training=False,
automated_pipeline=False,
automated_cicd=False)
print(f"Level {level}: {advice}")python3 main.py