Design AI feature launch and data collection
System Design: From AI Prototype to Production
Design a production-grade system for a user-facing AI-powered feature in a web/mobile product. The system must handle both real-time (online) predictions and periodic (batch) inferences. Key considerations include reliability, safety, privacy, and continuous improvement.
Your design should cover:
- Deployment Architecture: Differentiate between online and batch inference, define service boundaries (feature store, inference service, retrieval/ranking, data pipelines), and address scaling, availability, and failure isolation.
- Model and Feature Versioning: Implement strategies for versioning models, datasets, features, and transformations to ensure reproducibility and lineage.
- CI/CD for Models (MLOps): Establish a robust training/validation pipeline with automated tests and promotion gates, along with effective packaging and environment management.
- Monitoring and Observability: Define Service Level Indicators/Objectives (SLIs/SLOs) for latency, throughput, and errors. Monitor for data/feature drift, training-serving skew, model quality, and resource costs.
- Rollouts and Safety: Implement safe deployment strategies such as shadow, canary, blue/green, and A/B testing. Define guardrails and rollback procedures.
- Data and Feedback Instrumentation: Specify what data to log during inference and training. Design a labeling strategy (in-product feedback, delayed outcomes, active learning) and address consent, privacy, compliance, and data retention.
Be explicit about trade-offs and include practical guardrails and fallbacks.
AI interview prep
Core dilemma
Balancing the low-latency requirements of online inference with the resource-intensive nature of model training and batch processing, while ensuring data privacy and model safety across the entire ML lifecycle.
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 AI feature launch and data collection
System Design: From AI Prototype to Production
Design a production-grade system for a user-facing AI-powered feature in a web/mobile product. The system must handle both real-time (online) predictions and periodic (batch) inferences. Key considerations include reliability, safety, privacy, and continuous improvement.
Your design should cover:
- Deployment Architecture: Differentiate between online and batch inference, define service boundaries (feature store, inference service, retrieval/ranking, data pipelines), and address scaling, availability, and failure isolation.
- Model and Feature Versioning: Implement strategies for versioning models, datasets, features, and transformations to ensure reproducibility and lineage.
- CI/CD for Models (MLOps): Establish a robust training/validation pipeline with automated tests and promotion gates, along with effective packaging and environment management.
- Monitoring and Observability: Define Service Level Indicators/Objectives (SLIs/SLOs) for latency, throughput, and errors. Monitor for data/feature drift, training-serving skew, model quality, and resource costs.
- Rollouts and Safety: Implement safe deployment strategies such as shadow, canary, blue/green, and A/B testing. Define guardrails and rollback procedures.
- Data and Feedback Instrumentation: Specify what data to log during inference and training. Design a labeling strategy (in-product feedback, delayed outcomes, active learning) and address consent, privacy, compliance, and data retention.
Be explicit about trade-offs and include practical guardrails and fallbacks.