Unsolved Problems in MLOps: Why Classical Software Rules Fail for AI

Unsolved Problems in MLOps

We explore why standard software engineering practices fail in the world of machine learning. Unlike deterministic classical systems, ML models depend heavily on data, making reliability, deployment, and error resolution incredibly difficult. We highlight the critical gaps in our current frameworks for measuring model quality and managing production systems safely.

"Rolling out a new version of a model is often more an exercise in vibes than anything else."