Design a feature store with CI/CD and reliability
System Design: Feature Store for Offline Training and Low-Latency Online Inference
Context
Design a feature store to support machine learning models on an internet-scale social platform. This platform involves entities like users, items/content, and communities, and hosts models for recommendations, feed ranking, and abuse/spam detection. The feature store must cater to two primary use cases:
- Offline training on historical data.
- Low-latency online inference at high Queries Per Second (QPS).
Requirements
Your design should address the following aspects:
- Functional and Non-Functional Requirements: Define explicit functional requirements, Non-Functional Requirements (NFRs), Service Level Objectives (SLOs), and scale assumptions for the system.
- Feature Ingestion: Detail the mechanisms for ingesting both batch and streaming data into the feature store.
- Computation and Storage Layers: Design the offline and online computation and storage layers, ensuring point-in-time correctness for feature values.
- Feature Serving APIs: Specify APIs for generating training data and for serving features for online inference.
- Offline-Online Consistency: Propose a strategy to maintain consistency between offline and online feature stores.
- Data Management: Address freshness Service Level Agreements (SLAs), strategies for backfills, and bootstrap flows for new features or models.
- Metadata and Governance: Include provisions for feature lineage, metadata management, data governance, and access control.
- Deployment Architecture: Outline the overall deployment architecture and define different environments (e.g., dev, staging, production).
- CI/CD: Design a Continuous Integration/Continuous Deployment (CI/CD) pipeline for feature definitions and their associated data pipelines.
- Testing Strategies: Describe comprehensive testing strategies, including unit, integration, end-to-end tests, and data validation.
- Caching: Incorporate caching layers and define appropriate eviction policies.
- Reliability and Fault Tolerance: Detail mechanisms for ensuring system reliability, meeting SLOs, and handling fault tolerance.
- Operations: Plan for monitoring, alerting, and rollback processes.
- Cost and Rate Limiting: Consider cost implications and implement rate-limiting strategies.
- Technology Choices: Provide component diagrams and justify your technology choices for each part of the system.
AI interview prep
Core dilemma
Balancing the need for massive historical data processing for offline training with ultra-low-latency, high-QPS serving for online inference, all while maintaining consistency, freshness, and operational robustness at internet scale.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a feature store with CI/CD and reliability
System Design: Feature Store for Offline Training and Low-Latency Online Inference
Context
Design a feature store to support machine learning models on an internet-scale social platform. This platform involves entities like users, items/content, and communities, and hosts models for recommendations, feed ranking, and abuse/spam detection. The feature store must cater to two primary use cases:
- Offline training on historical data.
- Low-latency online inference at high Queries Per Second (QPS).
Requirements
Your design should address the following aspects:
- Functional and Non-Functional Requirements: Define explicit functional requirements, Non-Functional Requirements (NFRs), Service Level Objectives (SLOs), and scale assumptions for the system.
- Feature Ingestion: Detail the mechanisms for ingesting both batch and streaming data into the feature store.
- Computation and Storage Layers: Design the offline and online computation and storage layers, ensuring point-in-time correctness for feature values.
- Feature Serving APIs: Specify APIs for generating training data and for serving features for online inference.
- Offline-Online Consistency: Propose a strategy to maintain consistency between offline and online feature stores.
- Data Management: Address freshness Service Level Agreements (SLAs), strategies for backfills, and bootstrap flows for new features or models.
- Metadata and Governance: Include provisions for feature lineage, metadata management, data governance, and access control.
- Deployment Architecture: Outline the overall deployment architecture and define different environments (e.g., dev, staging, production).
- CI/CD: Design a Continuous Integration/Continuous Deployment (CI/CD) pipeline for feature definitions and their associated data pipelines.
- Testing Strategies: Describe comprehensive testing strategies, including unit, integration, end-to-end tests, and data validation.
- Caching: Incorporate caching layers and define appropriate eviction policies.
- Reliability and Fault Tolerance: Detail mechanisms for ensuring system reliability, meeting SLOs, and handling fault tolerance.
- Operations: Plan for monitoring, alerting, and rollback processes.
- Cost and Rate Limiting: Consider cost implications and implement rate-limiting strategies.
- Technology Choices: Provide component diagrams and justify your technology choices for each part of the system.