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
Scaling Offline Computation
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- Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Haoyuan Li, Scott Shenker, and Ion Stoica, 2012 USENIX Conference on Networked Systems Design and Implementation (NSDI) DOI: 10.5555/2632222.2632227 - Describes the core Resilient Distributed Dataset (RDD) abstraction, which is fundamental to Apache Spark's distributed computation model and fault tolerance capabilities.
- Apache Spark Documentation, The Apache Software Foundation, 2024 - Provides comprehensive technical details, usage guides, and optimization strategies for Apache Spark, covering its APIs, configuration, and performance tuning for large-scale data processing.
- Apache Flink Documentation, The Apache Software Foundation, 2025 - Offers complete documentation for Apache Flink, including its batch processing capabilities, Table API & SQL, and deployment guides relevant for large-scale feature computation.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Offers a systematic overview of the principles underlying reliable, scalable, and maintainable data systems, including topics like data models, distributed transactions, and batch processing architectures.
- Michelangelo: Uber's Machine Learning Platform, Sibylle Lanz, Jeremy H. Hong, Kai Jiang, Mayur Rustagi, Gaurav Singh, Mike Wu, David Xiao, and Andrew P. Zang, 2017 2017 IEEE International Conference on Data Science and Advanced Analytics (DASFAA) (IEEE) DOI: 10.1109/DSAAE.2017.8286940 - Presents Uber's end-to-end machine learning platform, Michelangelo, detailing its architecture and components, including the feature store, which relies on scalable offline computation. The URL points to an accessible summary of the paper.