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Unsolved Problems in MLOps: The Gap Between Theory and Production

While the industry has made significant strides in treating Machine Learning as a first-class citizen in the software lifecycle, the transition from a Jupyter Notebook to a robust production system remains fraught with peril. Many practitioners mistakenly believe that MLOps is simply "DevOps for ML"; in reality, it is a distinct discipline dealing with the non-deterministic nature of data and the stochastic behavior of models.

1. The Data-Code Duality Paradox

In traditional software, the binary is the sole artifact of truth. In MLOps, the "truth" is a tripartite intersection of Code, Data, and Hyperparameters.

The Versioning Struggle

Versioning code with git is solved. Versioning multi-terabyte datasets, however, remains an open wound. While tools like DVC or LakeFS attempt to bridge this gap, the industry still struggles with "Atomic Versioning"—the ability to perfectly snapshot the exact state of a data pipeline at the moment of training.

2. The Monitoring Mirage and Concept Drift

Monitoring a REST API is simple: is it returning a 200 OK? Monitoring a model is an exercise in statistical inference. A model can be technically "healthy" (low latency, no crashes) while being **functionally