MLOps Guide: How to Deploy, Monitor, and Prevent Silent ML Model Failures

A 2020 fraud detection model at a financial firm achieved 99.2% accuracy in testing but caused a 300% spike in false positives within a week of deployment, because pandemic-driven spending shifts made its training data obsolete. This real-world failure illustrates the core challenge of MLOps — machine learning models degrade silently over time as real-world data distributions change. Unlike traditional software bugs, model drift goes undetected without continuous monitoring, often surfacing only after customers are already affected. MLOps addresses this through a six-stage lifecycle covering data preparation, experimentation, validation, deployment, monitoring, and retraining — with most teams struggling at the final two stages. Hardware considerations also play a key role, as modern models require significant GPU VRAM, with a 7-billion-parameter model alone needing roughly 16–20 GB for reliable inference.