MLOps

MLOps (Machine Learning Operations) encompasses practices, tools, and organizational processes for deploying, monitoring, and maintaining machine learning models in production environments reliably and efficiently. Job listings requiring MLOps expertise reflect organizations moving beyond experimental ML toward operationalized systems where model performance, drift detection, and retraining pipelines matter as much as initial accuracy. ML engineers and platform engineers are expected to implement CI/CD pipelines for model training and deployment, build feature stores for consistent data access, establish monitoring for data quality and prediction drift, and create frameworks for A/B testing and gradual rollouts. The discipline borrows heavily from DevOps principles while addressing ML-specific challenges like reproducibility, versioning datasets alongside code, and handling non-deterministic behavior. Roles often involve selecting and integrating tools like MLflow, Kubeflow, or vendor platforms, implementing model registries and metadata tracking, and establishing governance processes for model approval and compliance. Companies hiring for MLOps skills typically run multiple models in production, experience pain from manual deployment processes, or operate in regulated industries requiring audit trails and explainability.