5Automating and orchestrating ML pipelines

• 5.1End-to-end ML pipelines

  Understand data and model validation, consistent preprocessing between training and serving, hosting third-party pipelines (MLFlow), identifying components/parameters/triggers/compute (Cloud Build, Cloud Run), orchestration frameworks (Kubeflow Pipelines, Vertex AI Pipelines, Cloud Composer), hybrid/multicloud strategies, and system design with TFX components or Kubeflow DSL.

• 5.2Retraining and metadata tracking

  Understand determining an appropriate retraining policy, CI/CD model deployment (Cloud Build, Jenkins), tracking/comparing model artifacts and versions (Vertex AI Experiments, Vertex ML Metadata), hooking into model/dataset versioning, and model and data lineage.