Toolbox

Feature Store & Feature Ops Design Checklist

Interactive checklist to evaluate feature engineering, discoverability, versioning, online/offline parity, freshness, testing, monitoring, governance, and operational runbooks for production-ready features.

Interactive Tool

Feature Store & Feature Ops Design Checklist

Use this interactive checklist to assess and document the readiness of features for training and production. Each item captures a quick yes/no assessment, owner, evidence, priority, and corrective actions so teams can reduce training/serving skew, speed iteration, and operate features reliably.

Canonical name used in the feature catalog or metadata store (e.g., customer_ltv_weekly).
Person or team responsible for the feature's lifecycle and operational health.
Catalog entry includes description, schema, tags, owners, lineage, and usage examples.
Includes data types, nullability, allowed values, and change policy.
Link or identifier for the schema version used for training and serving.
Lineage should show raw sources, transformations, enrichment steps, and dependent features.
Where is the feature computed and stored (batch, streaming, hybrid)?
Feature compute logic and preprocessing must match between training pipelines and serving. Describe differences if any.
Explain any mismatches and planned mitigations.
E.g., max staleness, acceptable end-to-end latency, SLOs.
Tests should validate transformations, null-handling, distributions, and parity between offline and online outputs.
List test types and locations (repo, pipeline, CI).
Monitoring should detect data distribution shifts, missing values, or feature value spikes that could affect models.
Ensure clear on-call and remediation steps.
Controls for who can read, write, and modify both metadata and feature data.
Document any transformation applied to meet privacy or compliance requirements.
Include step-by-step rollout checklist, validation gates, rollback criteria, and communication plan.
Link to runbook, runbook ticket template, or wiki page.
Include recent metrics or dashboards.
Automated tests and deployments reduce human error and drift.
Understand storage, compute, and serving cost tradeoffs.
Plan for removing unused or harmful features safely.
E.g., GDPR, HIPAA, financial regulations, or contractual restrictions.
Paste links to dashboards, test runs, PRs, or datasets that justify the assessments above.
Who will do what, by when, and how to verify completion.
YYYY-MM-DD or relative cadence (e.g., 90 days).
Summary assessment combining safety, parity, observability, and governance.
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