Advanced Feature Store Implementation for ML Systems
Chapter 1: Feature Store Architectural
Core Components Revisited: Advanced Perspectives
Online Store Architectures for Low Latency
Offline Store Design for Scalability
Metadata Management Strategies
Decoupled vs. Integrated Architectures
Cross-Region and Multi-Cloud Considerations
Practice: Designing a Scalable Architecture
Chapter 2: Advanced Feature Engineering and Computation
Feature Transformation Pipelines
Handling Streaming Features
Managing Embeddings and Unstructured Data
Time-Window Aggregations at Scale
On-Demand Feature Computation
Batch vs. Real-time Computation Trade-offs
Hands-on Practical: Implementing Complex Transformations
Chapter 3: Ensuring Data Consistency and Quality
Diagnosing and Mitigating Online/Offline Skew
Point-in-Time Correctness for Training Data
Advanced Data Validation Techniques
Monitoring Feature Data Distribution
Backfilling Strategies and Challenges
Consistency Guarantees in Distributed Systems
Practice: Implementing Skew Detection
Chapter 4: Performance, Scalability, and Optimization
Benchmarking Feature Store Performance
Optimizing Online Serving Latency
Scaling Offline Computation
Storage Optimization and Cost Management
High Availability and Disaster Recovery Patterns
Capacity Planning and Load Testing
Hands-on Practical: Tuning Online Store Performance
Chapter 5: Governance, Security, and MLOps Integration
Implementing Feature Governance Frameworks
Feature Versioning Strategies
End-to-End Feature Lineage Tracking
Access Control and Security Models
Integrating Feature Stores with CI/CD Pipelines
Feature Discovery and Cataloging
Auditing and Compliance Considerations
Practice: Setting Up Feature Lineage
Chapter 6: Evaluating and Operating Feature Store Solutions
Comparing Open-Source Feature Stores (e.g., Feast)
Analyzing Managed Feature Store Services (Cloud)
Build vs. Buy Decision Framework
Operational Monitoring and Alerting
Debugging Common Feature Store Issues
Team Structure and Roles for Feature Store Management
Hands-on Practical: Evaluating a Managed Service
Decoupled vs. Integrated Architectures
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- Tecton: A Fully Managed Feature Store for Real-time ML, Mike Sculley, Paul O'Rourke, Greg Demitriades, Josh Lee, Nick Frosst, Jeremy Hylton, Mike S. Kim, Sam Charrington, Ben Van Durme, Kevin Jue, Matt Glickman, 2020 Proceedings of the VLDB Endowment, Vol. 13 (VLDB Endowment) DOI: 10.14778/3407790.3407817 - Describes the architecture and advantages of a managed, integrated feature store, emphasizing consistency and operational ease.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - A guide to building ML systems, including architectural decisions for data management and component integration, relevant to both integrated and decoupled approaches.
- Feast Documentation: Architecture Overview, Feast Project Maintainers, 2025 (Feast Project) - Explains how Feast acts as an orchestration layer for independently chosen components, illustrating a decoupled feature store architecture.
- MLOps: A Taxonomy and Survey, Mohamed Ibrahim and Maedeh Saeedi, 2022 Future Generation Computer Systems, Vol. 136 (Elsevier) DOI: 10.1016/j.future.2022.06.012 - Provides a review of MLOps architectural patterns and challenges, including data infrastructure and system integration considerations.