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
Capacity Planning and Load Testing
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- Site Reliability Engineering: How Google Runs Production Systems, Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Murphy, 2017 (O'Reilly Media) - Provides foundational principles for designing and operating reliable, scalable systems, including Service Level Objectives (SLOs), capacity planning, and various testing strategies applicable to feature stores.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Explains core concepts and trade-offs for building distributed data systems that are highly available, scalable, and performant, which are critical considerations for the underlying components of a feature store.
- MLOps Engineering at Scale, Sumit Sethuraman, Sumit Kumar, Sanjeev Singh, Anoop Sharma, Sayan Patra, 2022 (O'Reilly Media) - Offers practical guidance on implementing MLOps, including considerations for performance, scalability, monitoring, and operational best practices for machine learning systems like feature stores.
- AWS Well-Architected Framework - Performance Efficiency Pillar, Amazon Web Services, 2022 (Amazon Web Services) - Provides architectural guidance for achieving performance efficiency in cloud environments, covering resource selection, monitoring, and scaling strategies relevant to capacity planning for cloud-native feature stores.