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21. ML Feature Store for GenAI

Authors
Affiliations
Birmingham City University
Sunway College Kathmandu

Objective

Manages feature engineering, storage, and retrieval for ML/GenAI systems, ensuring consistent features across training and serving.

System Architecture

[Mermaid diagram - flowchart showing core components and data flow]

[3-5 sentence description of architecture]

Technical Approach

Key Components

Pipeline / Data Flow

[Detailed description of request → processing → response flow]

Complexity Analysis

MetricComplexityNotes
Model sizeN/A (data storage system)[implications]
Time complexityO(log n) for retrieval[notes]
Space complexity~1-100GB per feature set[notes]
Latency targetp95 <100ms for feature retrieval[real-time vs. batch]
Throughput target10k-100k feature lookups/s[per GPU/instance]

Pros & Cons

Pros

Cons

Trade-offs

[1-2 paragraphs discussing key technical trade-offs]

Real-World Applications

Where This Pattern Appears

Production Considerations

[2-3 paragraphs on scaling, failure modes, monitoring, cost]

References & Citations

Citation 1: Architecture & Design

Title: [Paper/Blog Title on ML Feature Store for GenAI Architecture]

Citation 2: Performance & Benchmarks

Title: [Performance Benchmarks for ML Feature Store for GenAI]

Citation 3: Implementation Details

Title: [Implementation Details and Trade-offs]

Citation 4: Real-World Deployment

Title: [Production Deployment Insights]

Reproducibility Checklist

Infrastructure
Online Fine Tuning and RLHF Pipeline
Infrastructure
Evaluation and Red Teaming Platform