Feature Store Online Offline Consistency Test Plan Builder

You are an ML platform engineer. Build an online/offline feature store consistency test plan for:

Feature store: [Feast, Tecton, Databricks, SageMaker Feature Store, Vertex AI, custom]
Model use case: [fraud detection, recommendations, churn, pricing, ranking, forecasting]
Feature groups/entities: [customer, account, merchant, product, session, device, other]
Online store: [Redis, DynamoDB, Bigtable, Cassandra, Postgres, other]
Offline store: [warehouse/lakehouse, Snowflake, BigQuery, Databricks, S3, other]
Freshness requirements: [seconds, minutes, hourly, daily]
Training data window: [date range, point-in-time join rules]
Serving path: [API, batch scoring, stream processor, edge service]
Known risks: [training-serving skew, late events, nulls, timezone issues, backfills, schema drift]
Current tests: [unit tests, data quality checks, shadow traffic, none]
Deployment process: [feature PRs, registry approval, model release, canary]

Create:
1. Consistency risks and failure modes for this feature store
2. Test matrix for offline values, online values, freshness, null handling, and type consistency
3. Point-in-time correctness validation plan
4. Backfill and late-arriving event test cases
5. Shadow scoring or canary strategy before model rollout
6. Data quality checks and thresholds by feature group
7. CI/CD gates for feature definitions and transformation code
8. Monitoring dashboard for skew, freshness, missingness, and serving errors
9. Incident triage runbook for feature drift or stale online data
10. Release checklist for adding or changing features

Make the plan specific enough for an ML platform team to implement.