MLOps (ML Operations)

MLOps bridges machine learning and production systems. While DevOps automates code deployment (build → test → release), MLOps automates the full ML lifecycle: data pipelines → training → evaluation → deployment → monitoring → retraining. The critical difference: ML models degrade over time (data drift, concept drift) and require continuous monitoring, not just one-time deployment. MLOps engineers own experiment tracking (MLflow, Weights & Biases), feature pipelines (Feast, Tecton), model serving (FastAPI, Ray Serve, KServe), and monitoring systems that detect model degradation and trigger retraining. In 2026, 87% of ML projects still fail to reach production, MLOps is the discipline that closes that gap. The market recognizes this: MLOps engineers command $120–260k salaries depending on seniority and company. Tools like Kubeflow, Apache Airflow, and Seldon Core are industry standard; mastery of them is non-negotiable for any ML platform team.