Product Context

Pulse is a consumer subscription app with a growth team that scores users in real time for interventions such as upgrade prompts, referral offers, win-back notifications, and paywall variants. The core challenge is designing a feature store that serves low-latency, fresh, and training-consistent features to multiple online growth models.

Scale

The growth platform currently runs models for conversion propensity, churn risk, notification send-time optimization, and offer ranking. Product and marketing teams want one shared feature platform instead of bespoke pipelines per model.

Task

1. Define the functional and non-functional requirements for a real-time feature store supporting growth ML use cases.
2. Design the end-to-end architecture, including event ingestion, feature computation, online/offline storage, training data generation, and online serving.
3. Explain how the feature store integrates with a multi-stage decision system for growth actions (eligibility/filtering → scoring/ranking → policy constraints).
4. Propose model and feature patterns that work well for this setup, including handling sparse users, cold start, and delayed labels.
5. Describe evaluation, monitoring, and rollback strategies, with explicit treatment of feature drift, training-serving skew, and data quality failures.

Constraints

• User-level features may include PII-derived attributes, so the system must support deletion requests and access controls.
• Some features must update in near real time; others can be batch-computed hourly or daily to control cost.
• The online path must degrade gracefully if the feature store or stream processor is delayed.
• Teams need point-in-time correct offline features for training and backfills.
• Cost target: keep feature serving + scoring under $0.0015 per prediction at peak load.