Sort ML Artifacts by Owner
Assign basic ML workflow artifacts to likely owners and identify handoff risks.
Sort each artifact by the team that should usually be accountable. Real orgs vary; the point is to expose handoff gaps. Data Science Data Engineering Platform Engineering Product or Business Owner Risk or Compliance Training notebook and model selection rationale Feature table freshness and upstream data quality checks Model serving endpoint uptime and rollback mechanism Business acceptance metric and pilot scope Fairness review and approved-use boundaries Experiment run comparison and candidate recommendation Batch pipeline orchestration and partition completeness Incident paging route for inference service failures Decision threshold tradeoff for sales workload Model card review for out-of-scope use
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