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
Core Components Revisited: Advanced Perspectives
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - This book provides a practical approach to building ML systems, with specific content on feature engineering, data management, and the function of feature stores in supporting data quality, lineage, and operational efficiency for production ML applications.
- Managing Data Schema Changes in Production Machine Learning Pipelines, Jonathan R. S. Johnson, Emily M. Reif, Christopher J. Olaes, Jason D. Hibbs, Douglas E. Zongker, 2021 Proceedings of the Workshop on Data Management for End-to-End Machine Learning (DEEM '21) at KDD '21 (ACM) DOI: 10.1145/3447953.3460831 - This article addresses the challenges of schema evolution in ML data pipelines, offering strategies for managing changes to feature definitions and their impact on downstream models, a main function of the Feature Registry.
- Delta Lake: High-Performance ACID Table Storage for Spark and Beyond, Michael Armbrust, Sameer Agarwal, Xiangrui Meng, Timothy Hunter, Joseph K. Bradley, Ali Ghodsi, Andrea J. Hu, Tathagata Das, Databricks Team, 2020 Proceedings of the VLDB Endowment, Vol. 13 (VLDB Endowment) DOI: 10.14778/3407790.3407823 - This article introduces Delta Lake, an open-source storage layer that brings ACID transactions and time-travel capabilities to data lakes, supporting the 'point-in-time correctness' needed by the offline feature store for reproducible training data.