Checklist

Feature Store / Feature Engineering Readiness Checklist

Interactive checklist to evaluate whether candidate features and pipelines are ready for production feature stores and ML serving. Capture evidence, owner, notes, and an overall readiness score so teams can track and improve feature maturity over time.

Interactive Tool

Feature Store / Feature Engineering Readiness Checklist

Use this checklist to evaluate whether a candidate feature and its pipelines are ready for production in a feature store and for ML serving. Record evidence links, owners, notes, and an overall readiness score so teams can track progress, reproduce decisions, and avoid mismatches between training and serving.

Canonical name used in catalog or code (for example, user.avg_30d_spend).
Optional internal ID, tracking tag, or catalog GUID.
Which environment does this assessment apply to?
Primary owner responsible for feature lifecycle, incidents, and handoffs.
Are source datasets, transformation steps, and lineage documented (diagrams, code links, or lineage system)?
Link to docs, lineage graph, PR, or code proving provenance.
Are freshness SLAs, expected latency, and acceptable staleness defined for both training and serving?
Is the feature definition precise (aggregation window, unit, normalization) so training and serving compute the same value?
Are input data checks, thresholds, alerting, and remediation paths defined for data sources and computed feature values?
Are there automated tests that confirm batch (training) values and serving (online) values match within tolerances?
Are PII, consent, retention, minimization, and regulatory constraints identified and mitigations implemented?
Is there a versioning scheme for feature definitions, migration plan, and rollback process for serving?
Is the feature discoverable in the catalog with metadata, owner, and access controls?
Capture risks, open actions, drift concerns, or contextual details reviewers should know.
Rate 1 (Not ready) to 5 (Ready for production)
1.0 10.0
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