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
On-Demand Feature Computation
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- Managed Feature Store for Machine Learning, Sean H. Lee, Michelle Mao, Jiangong Shen, Jie Sun, 2020 2020 IEEE International Conference on Data Mining Workshops (ICDMW) (IEEE) DOI: 10.1109/ICDMW51313.2020.00057 - Describes Google's approach to a managed feature store, discussing strategies for feature computation, storage, and serving, including aspects relevant to on-demand scenarios in a cloud environment.
- Hidden Technical Debt in Machine Learning Systems, D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Michael Chaudhary, Cora Fairchild, Jean-Philippe Franco, Alan Horn, Vivek Hutchinson, Sunny Karanth, Todd Kwon, Nicolas Levin, David Lopes, Artak Mkhitaryan, Shahar Marcus, Jeff Penn, Yuri Polyakov, Michael Smith, Sharat Suresh, Dave Taylor, Bojia Wang, Kevin Yang, 2015 Advances in Neural Information Processing Systems 28 (NIPS 2015) (NeurIPS) DOI: 10.5591/978-1-57766-700-1_237 - A foundational paper that identifies common challenges in real-world ML systems, including the critical issue of training-serving skew, which is particularly relevant to on-demand features.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - A comprehensive guide to building ML systems, providing context on how on-demand feature computation fits into overall system design, real-time inference, and data consistency strategies.