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
Operational Monitoring and Alerting
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- Site Reliability Engineering: How Google Runs Production Systems, Benjamin Treynor Sloss, Betsy Beyer, 2016 (O'Reilly Media) - Provides fundamental principles and practices for system reliability, monitoring, alerting, and incident response, applicable to complex production systems like feature stores.
- Tecton: A Modern Feature Store for ML, Mike Zadeh, Sam Steingold, Ben Zaitlen, Matt Gormley, Jeremy Hyrkas, Max Hjelm, Benji Cooper, Andrew Lee, Adarsha Nadig, Josh Rosen, Ryan Smith, Kevin Tian, Justin Trevor, Xiaoyong Wang, Mike Williams, David Li, 2021 Proceedings of the VLDB Endowment, Vol. 14 (VLDB Endowment) DOI: 10.14778/3476311.3476344 - Describes a modern feature store architecture, including components that require operational monitoring and touches on reliability for ML systems.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Explores principles for building reliable, scalable, and maintainable data systems, which form the foundation of feature stores, including concepts of consistency, availability, and their operational implications.
- Reliable Machine Learning: Applying SRE Principles to ML in Production, Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood, 2022 (O'Reilly Media) - Focuses on applying Site Reliability Engineering (SRE) principles specifically to machine learning systems, including data quality monitoring, model monitoring, and ensuring operational reliability of ML pipelines.