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
Feature Discovery and Cataloging
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - Provides comprehensive coverage of feature stores, including the importance of metadata, discovery, and governance within the broader context of MLOps system design.
- Amundsen: A Data Discovery Platform for the AI Era, V. Song, H. Hacigundogdu, S. Kulkarni, B. Hu, V. Kunjir, X. Deng, K. C. Lee, R. Lin, 2019 Proceedings of the VLDB Endowment (PVLDB), Vol. 12 (VLDB Endowment) DOI: 10.14778/3352063.3352077 - Introduces Lyft's open-source data discovery platform, detailing its architecture and how it facilitates metadata management and feature discoverability.
- DataHub: A Metadata Platform for the Modern Data Stack, X. Deng, C. P. Binnig, S. Gupta, K. C. Lee, Z. Luo, S. Kulkarni, V. Song, V. Kunjir, 2020 Proceedings of the VLDB Endowment, Vol. 13 (VLDB Endowment) DOI: 10.14778/3415494.3415555 - Presents LinkedIn's open-source metadata platform, offering insights into building a scalable system for data and feature cataloging, lineage, and governance.
- Vertex AI Feature Store documentation, Google Cloud, 2024 - Official documentation for a major cloud provider's feature store, illustrating practical aspects of feature management, including discovery, within a managed service.