Quick answer: Build + monitor production ML pipelines — model registry, drift, retraining.
MLOps Pipelines are the infrastructure that keeps machine learning models running reliably in production. They automate the entire lifecycle: training models on fresh data, validating performance, detecting when models degrade (drift), and automatically retraining when needed. Instead of manually checking if your recommendation engine still works or your fraud detector catches new patterns, MLOps pipelines do this continuously. You'll work with model registries (storing versioned models), monitoring systems (tracking accuracy over time), and orchestration tools like Airflow or Kubeflow. For example, an Indian fintech platform might use MLOps to retrain its credit-scoring model weekly as user behavior shifts, or an e-commerce site might auto-detect when product recommendation accuracy drops and trigger retraining automatically.