What's changed: Created Professional Machine Learning Engineer Chapter 5 (Domain 5 "Pipelines": data/model validation, consistent preprocessing, MLFlow hosting, components/parameters/triggers/Cloud Build/Cloud Run, orchestration (Kubeflow Pipelines/Vertex AI Pipelines/Cloud Composer), hybrid/multicloud, TFX/Kubeflow DSL; retraining policy, CI/CD (Cloud Build/Jenkins), Vertex AI Experiments/Vertex ML Metadata, model/data versioning, lineage).
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.
A model is not done once built. Retrain as it degrades, deliver safely via CI/CD, and track every artifact for reproducibility and auditability.
5.2.1Retraining policy and CI/CD
Models degrade (drift) over time, so set a retraining policy: periodic (scheduled) retraining or monitoring-triggered retraining (on performance/drift), chosen by cost and degradation speed. Test, validate, and deploy retrained models via CI/CD (Cloud Build or Jenkins), automated as continuous training (CT). Switch safely with A/B/canary. Map "retrain on degradation = monitoring-triggered retraining" and "automate model test/deploy = CI/CD (Cloud Build/Jenkins)."
5.2.2Metadata, versioning, and lineage
For reproducibility and audit, track all artifacts. Compare experiment params/metrics with Vertex AI Experiments, and record pipeline-produced artifacts/executions/lineage with Vertex ML Metadata. Hook model and dataset versioning together so you can trace lineage—"which data, which code, which parameters produced this model." This enables root-cause analysis on incidents and regulatory compliance. Map "record artifacts/executions/lineage = Vertex ML Metadata" and "trace a model's origin = lineage."
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