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
Implementing Feature Governance Frameworks
Was this section helpful?
- Hidden Technical Debt in Machine Learning Systems, D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison, 2015 Advances in Neural Information Processing Systems 28 (NIPS 2015) (Curran Associates, Inc.) - A foundational paper identifying non-ML components of ML systems that contribute to maintenance and operational issues, many of which feature governance aims to address.
- Data Governance: The Definitive Guide, Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021 (O'Reilly Media) - This 2nd edition provides a comprehensive framework for data governance, covering principles like data quality, ownership, and compliance, which are directly applicable to feature governance.
- Data Quality for Machine Learning: An Introduction and Survey, Vedrana Stoyanovich, Felix Naumann, Saravanan Thirumuruganathan, H. V. Jagadish, Gerome Miklau, Divesh Srivastava, 2020 VLDB Journal, Vol. 29 (Springer Berlin Heidelberg) DOI: 10.1007/s00778-020-00626-9 - A survey on data quality challenges and solutions specifically for machine learning pipelines, essential for ensuring feature reliability.
- MLOps Engineering at Scale, Kedar Sethuraman, Vignesh Kothari, 2022 (O'Reilly Media) - This book offers practical guidance on building and operating production ML systems, including detailed sections on feature stores and their management within an MLOps framework.