If you've ever shipped a machine learning model to production, you know the feeling. Everything works beautifully in your notebook, the metrics look great in staging, and then... three weeks after deployment, accuracy quietly tanks. Nobody notices until a stakeholder asks why the recommendations got weird.
This is the gap traditional software development practices don't fill. SDLC was built for deterministic systems—code that does the same thing every time. ML systems aren't deterministic, they're statistical. They decay. They drift. They need to be retrained on schedules that have nothing to do with feature releases.
Enter the AI Development Life Cycle (AIDLC).
What AIDLC Actually Is
AIDLC is a structured framework for building, deploying, and maintaining AI systems. It borrows the discipline of SDLC but adds the loops and feedback mechanisms that ML systems actually need.
