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 Versioning Strategies
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- TFX: A TensorFlow-Based Production Machine Learning Platform, Clemens Mewald, Robert Crowe, Noah Fiedel, Konstantin Goldeli, Joshua T. Gordon, Denis Khayda, D. Sculley, Ben Vanik, and Jeremy Zurcher, 2019 KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Association for Computing Machinery (ACM)) DOI: 10.1145/3292500.3330663 - Describes Google's TFX platform, which established principles for production ML, including feature management and the need for data and model versioning to ensure reproducibility and reliability in large-scale systems.
- Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores, Matei Zaharia, Tathagata Das, Rahul Goswami, Joseph Bradley, Xiangrui Meng, Liwen Sun, Burak Yavuz, Arsalan Tavakoli, Ali Ghodsi, and Michael Armbrust, 2020 Proceedings of the VLDB Endowment, Vol. 13 (VLDB Endowment) DOI: 10.14778/3415494.3415512 - This paper introduces Delta Lake, a technology that enables time-travel queries and ACID transactions on data lakes, which is fundamental to implementing time-based feature versioning strategies.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - Provides a guide to building and deploying ML systems, with sections that discuss feature management, data versioning, and the importance of reproducibility in MLOps workflows.
- Semantic Versioning 2.0.0, Tom Preston-Werner, 2013 - The official specification for semantic versioning, a widely adopted standard for software that can be adapted for versioning feature definitions and transformation code.