MLOps

MLOps (Machine Learning Operations) covers the infrastructure, processes, and tooling needed to deploy, monitor, and maintain ML models in production. It draws from DevOps (CI/CD, infrastructure-as-code), data engineering (pipelines, feature management), and ML engineering (model versioning, experiment tracking, serving).

The ML pipeline and experiment tracking that make training reproducible; feature stores that eliminate training-serving skew; CI/CD pipelines gated on data quality and evaluation thresholds; drift detection that tells you when a model has silently degraded; and serving patterns — from REST APIs to canary rollouts — that get a model safely in front of traffic.

Most ML projects fail not because of algorithmic problems, but because of operational ones: models trained on stale data, features computed differently in training vs production, no monitoring until users complain, manual deployment steps that introduce errors. MLOps addresses the 80% of ML work that happens after the model is trained. A 3% model improvement that takes a month to deploy adds less value than a 1% model improvement that deploys daily and catches drift automatically.

ML Engineer and senior data science roles test MLOps heavily. Expect system design questions: "design a real-time fraud detection pipeline," "how would you retrain a recommendation model daily at scale," or "how would you detect when a model has degraded?" Knowing the failure modes (training-serving skew, silent drift, feature leakage in pipelines, deployment rollback triggers) demonstrates production ML experience that differentiates candidates. Entry-level roles test familiarity with the concepts; senior roles test the tradeoffs and war stories.