mlops-engineer

Use orchestration tools like Kubeflow Pipelines, Airflow, or SageMaker Pipelines to chain data processing, training, evaluation, and deployment steps. Add automated triggers based on data changes or performance degradation, with approval gates before production rollout.

MLflow provides end-to-end lifecycle management with a built-in model registry, while Weights & Biases, Neptune, and Comet focus on experiment tracking. DVC adds Git-based data and model versioning for reproducibility.

Kubeflow is Kubernetes-native and purpose-built for ML workflows including training and serving, while Airflow is a general DAG orchestrator suited for complex data pipelines. Choose Kubeflow for containerized ML platforms and Airflow for broader data engineering integration.

Yes, it covers AWS SageMaker, Azure ML, and Google Vertex AI stacks, including their pipelines, registries, endpoints, and monitoring services. Terraform and other infrastructure-as-code tools enable multi-cloud provisioning.

Implement model performance monitoring with drift detection on input data distributions and prediction quality metrics. Use Prometheus, Grafana, or cloud-native monitoring, and trigger automated retraining pipelines when degradation thresholds are crossed.