The Feature Store That Kept AI From Hallucinating On Black Friday
A field-tested blueprint for deploying feature stores, guardrails, and instrumentation to safely run AI in production.
"When AI hits production, data quality is policy; a feature store is your safety net, not a data lake."Back to all posts
We learned this the hard way during the last major shopping weekend: a single unrenormalized feature in our online store feature view caused a cascade of hallucinations in the model, leading to incorrect recommendations and refunds that stressed our support load. The outage didn’t come from a fancy AI bug; it came from
a broken data contract, stale offline features, and a monitoring stack that went quiet under peak load. We did not fix AI with more training data; we fixed the production plumbing: a feature store as the single source of truth for data, guarded by policy, observed end-to-end, and protected by canary risk controls.
We turned to Feast as the backbone of our feature store and paired it with a robust data-contract framework, proving that the right schema plus validation could stop a bad feature from surfacing in production. We wired OpenTelemetry traces through the data path so feature age, retrieval latency, and score time traveled
every transaction could be traced back to a feature version. Then we layered guardrails—policy-as-code in OPA—that prevented the model from ever consuming features that failed quality gates, drift thresholds, or PII classification rules. The result: faster detection, safer experimentation, and a defense-in-depth that a
true observability stack can prove: the feature lineage, the model's calibration curves, and the end-to-end latency from feature fetch to prediction. By starting small—1% canary shipments and progressive exposure—we learned how to avoid the Friday catastrophe while keeping velocity intact.
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Key takeaways
- Feature stores unify data contracts and guardrails across all models, greatly reducing drift-induced errors.
- Instrumentation at retrieval and serving time is non-negotiable for accountability and MTTR.
- Runtime guardrails (policy-as-code) prevent unsafe feature delivery without killing velocity.
- Drift, hallucination, and latency spikes are operational problems, not one-off ML bugs; treat them with data-plane guardrails and SRE discipline.
- A phased rollout with canarying and feature-flag gating minimizes blast radius during modernization
Implementation checklist
- Define features as first-class data contracts with a trusted schema, validation rules, and drift metrics.
- Deploy a Feast-based feature store with offline/online stores and a centralized registry.
- Instrument all feature retrieval and scoring with OpenTelemetry and Prometheus; set SLOs for latency and staleness.
- Implement runtime guardrails with OPA/Kyverno to validate features before scoring.
- Establish drift and data quality checks in ingestion and at feature retrieval; alert on drift rate > threshold.
- Use canary deployments and feature flags to roll out new features to a small cohort before full enablement; automate rollback triggers at pulse of latency or drift spikes
Questions we hear from teams
- What exactly is a feature store and why do we need one for production AI?
- A feature store is a data layer that stores, serves, and governs feature data used by ML models; it provides data contracts, versioning, and lineage to keep AI accurate and auditable in production.
- How do we prevent drift and hallucination from surfacing in production?
- By enforcing data contracts, using real-time drift checks, integrating guardrails with policy-as-code, and monitoring feature-age and retrieval latency end-to-end.
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