TrialMLOps13 min
When you need this
- Three models duplicate feature SQL
- Retrain improved offline but degraded online
- Feature definitions live in notebooks
- No point-in-time correctness for training joins
Prerequisites
- Data warehouse or offline store
- One high-value shared feature set
- Online store option (Redis / Dynamo / etc.)
Tools
Pilot when 3+ models share features; skip for single-model teams.
Trial before auto-retrain; bad data amplification is the #1 silent failure mode.
Steps
- 1
Pick one feature family
Start with customer behavioral features used by 2+ models — not an entire catalog migration.
- 2
Define Feast entities and views
Point-in-time correct joins for training; online store for inference. Version feature definitions in Git.
- 3
Validate parity + data quality
Compare training batch vs online fetch for sample entities. Add Great Expectations checks on feature ranges before materialization.
- 4
Expand on proven ROI
Add features only after the first family shows reduced skew incidents or clear engineering time saved.
Adoption pitfalls
- Boiling the ocean with 200 features on day one
- No owner for feature freshness SLAs
- Skipping point-in-time joins → optimistic offline metrics
Adoption checklist
- Parity test in CI
- Feature owner documented
- Online store SLA monitored
- Rollback to legacy path tested
SEER REAL assessment / sprint
Assessment looks for skew incidents and duplicated feature SQL. Pilot sprint scopes one feature family through Feast with a parity test in CI.