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Design a Feature Store System

Build a centralized feature management platform that serves both real-time ML inference and batch training workloads at massive scale.

ML SystemsData InfrastructureFeature Engineering
Q: What scale of feature serving and training workloads do we need to support?
A: Support 1M+ real-time predictions per second with {'<'}10ms P99 latency, plus petabyte-scale batch training workloads processing 100K+ features across 1000+ ML models.
Engineering Implications: Massive scale drives dual-architecture design: optimized online store (Redis/DynamoDB) for low-latency serving and offline store (S3/Delta Lake) for batch processing. Need sophisticated caching, partitioning, and replication strategies.
Q: What types of features and transformations should we support?
A: Raw features (user demographics, transaction data), aggregated features (rolling windows, statistical summaries), and real-time features (session behavior, streaming events). Support time-based transformations and complex feature engineering.
Engineering Implications: Different feature types require different storage and computation patterns. Real-time features need streaming pipelines, aggregated features need efficient window computation, and historical features need versioning and point-in-time consistency.
Q: What are the consistency and freshness requirements?
A: Online features must be consistent with training data for model accuracy. Support point-in-time correctness for historical features and near-real-time freshness ({'<'}1 minute lag) for streaming features.
Engineering Implications: Training/serving skew is a major ML failure mode. Need versioning, lineage tracking, and synchronized pipelines to ensure features used in training match exactly what's served in production.
Q: How should we handle feature discovery and governance?
A: Centralized feature catalog with metadata, lineage, and quality metrics. Support feature sharing across teams, automated quality monitoring, and governance policies for feature lifecycle management.
Engineering Implications: Feature stores are organizational tools as much as technical ones. Need searchable catalog, feature ownership, quality SLAs, deprecation workflows, and collaboration tools to prevent duplicate work and ensure quality.
Q: What environments and deployment patterns are needed?
A: Multi-environment support (dev/staging/prod), feature flag integration, A/B testing support, and seamless promotion pipelines. Support both cloud and on-premises deployments with hybrid capabilities.
Engineering Implications: ML development requires safe experimentation and gradual rollouts. Need environment isolation, feature versioning, canary deployments, and rollback capabilities to support ML development lifecycle safely.
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🎯 Interview Practice Questions

Practice these follow-up questions to demonstrate deep understanding of feature store systems in interviews.

1. Training/Serving Skew Prevention

"Your feature store supports 1000+ ML models across multiple teams. How do you ensure zero training/serving skew when different teams use different transformation frameworks (Spark, Pandas, SQL)? Design a system that guarantees identical feature computation logic between training and serving."

2. Real-time Feature Pipeline

"Design a streaming feature pipeline that processes 100K events/second and updates features with <1 second latency. How do you handle late-arriving data, ensure exactly-once processing, and maintain consistency between streaming and batch-computed features?"

3. Multi-Tenant Feature Governance

"Your feature store serves 50+ teams with different data access levels and compliance requirements. How do you implement feature-level access control, audit feature usage, and prevent teams from accidentally using PII features in production models while enabling feature discovery and sharing?"

4. Feature Store Scaling Strategy

"Your online serving goes from 100K to 10M predictions/second over 6 months. How do you scale your Redis cluster, handle hot-spotting on popular features, and maintain <10ms P99 latency while managing costs? Design auto-scaling and caching strategies."

5. Time-Travel and Experimentation

"Data scientists need to backtest models with features as they existed 6 months ago and run A/B tests with different feature versions. How do you implement time-travel queries, feature versioning, and safe experimentation without impacting production serving?"

6. Cross-Region Feature Consistency

"Your feature store serves models globally with strict latency requirements. How do you replicate features across regions, handle network partitions between regions, and ensure eventual consistency while maintaining low latency? Design disaster recovery and failover strategies."

💡 ML System Design Interview Tips

Key strategies for discussing feature stores in ML system design interviews.

Technical Deep Dives

Dual Architecture Decision

Always explain why you choose online vs offline stores. Online optimized for low-latency lookups, offline for analytical workloads. Discuss Redis vs DynamoDB trade-offs for online serving.

Transformation Consistency

Emphasize training/serving skew prevention. Discuss shared transformation definitions, version control, and validation strategies to ensure identical feature computation.

Scale Calculations

Show concrete numbers: 1M QPS × 500 features = 500M lookups/sec. Calculate Redis cluster sizing, memory requirements, and network bandwidth needs.

Common Pitfalls

❌ Over-Engineering

Don't jump to complex streaming solutions immediately. Start with batch processing and add real-time features only when needed.

❌ Ignoring ML Lifecycle

Feature stores aren't just databases. Discuss experimentation, versioning, monitoring, and governance - the full ML development lifecycle.

❌ Missing Operational Concerns

Don't forget monitoring, alerting, cost optimization, and incident response. These are critical for production ML systems.