Common ML pipeline stages in Airflow / Kubeflow.
easyAnswer
- (1) Data ingestion / snapshot.
- (2) Validation (schema + drift check).
- (3) Feature engineering / feature store push.
- (4) Training (with distributed compute).
- (5) Evaluation (metrics + fairness).
- (6) Model validation vs baseline (regression guard).
- (7) Registration in model registry.
- (8) Approval (manual gate or automated).
- (9) Deploy to staging + integration tests.
- (10) Canary + progressive rollout.
- (11) Monitor + alert.
- Each stage is a task in DAG.
Check yourself — multiple choice
- Random
- Ingest → validate → features → train → evaluate → validate vs baseline → register → approve → deploy staging → canary → monitor; each = task in DAG
- Just train
- Not real
Pipeline stages: ingest / validate / features / train / eval / register / deploy / monitor.
#pipeline
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