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
Advanced Data Validation Techniques
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- Machine Learning Design Patterns, Valliappa Lakshmanan, Sara Robinson, Michael Munn, 2020 (O'Reilly Media) - A practical guide to building ML systems, including sections on feature stores and data validation strategies within the MLOps context.
- Deequ: A Library for Quality Constraints for ML, Krisztian Balog, Sebastian Michels, Tamas Seipp, Stephan Klinger, Felix Biessmann, Peter Schafhalter, 2020 Proceedings of the ACM Conference on Management of Data (SIGMOD) (ACM) DOI: 10.1145/3318464.3386131 - Introduces Deequ, an open-source library from Amazon for defining and validating data quality constraints using statistical methods in ML pipelines.
- A Survey on Concept Drift Adaption, Jawad Sajjad, Zahid Halim, Abdul Qayyum Khan, Shahzad Aslam, Muhammad Bilal, 2021 IEEE Access, Vol. 9 (IEEE) DOI: 10.1109/ACCESS.2021.3090885 - A comprehensive review of techniques for detecting and handling concept drift, which is important for statistical property validation and maintaining model performance.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - A foundational text discussing schema evolution, data encoding formats like Avro and Protobuf, and ensuring data consistency in distributed systems.