MLOps
DevOps but for ML models — training, versioning, deployment, monitoring of models in production.
Tools: MLflow (experiment tracking), Weights & Biases, Kubeflow (k8s-based), SageMaker. Specific challenges: model drift (the world changes), data lineage, reproducible training.