Design AI feature launch and data collection

Q: Design AI feature launch and data collection

This question evaluates a candidate's competency in designing production-grade ML systems, covering deployment architecture, online and batch inference, model and feature versioning, MLOps/CI-CD, monitoring and observability, rollout and safety strategies, and data/feedback instrumentation.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Technical Screen rounds at Intuit.

Q: What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Intuit during technical interviews.

Question

System Design: From AI Prototype to Production

Context

Assume you are designing a user-facing AI-powered feature for a web/mobile product. Some decisions must return predictions in real time (online inference), while others can be scheduled periodically (batch inference). Propose a production-grade design that addresses reliability, safety, privacy, and continuous improvement.

What to Cover

Deployment architecture
- Online vs. batch inference
- Service boundaries (feature service/store, inference service, retrieval/ranking, data pipelines)
- Scaling, availability, and failure isolation
Model and feature versioning
- Versioning for models, datasets, features, and transformations
- Reproducibility and lineage
CI/CD for models (MLOps)
- Training/validation pipeline, automated tests, promotion gates
- Packaging and environment management
Monitoring and observability
- Latency/throughput/error SLI/SLOs
- Data/feature drift, training-serving skew, model quality
- Cost/resource monitoring
Rollouts and safety
- Shadow, canary, blue/green, and A/B testing
- Guardrails and rollback procedures
Data and feedback instrumentation
- What to log at inference and training time
- Labeling strategy (in-product feedback, delayed outcome labels, active learning)
- Consent, privacy, compliance, and data retention

Be explicit about trade-offs and provide practical guardrails and fallbacks.

Design AI feature launch and data collection

Quick Overview

Design AI feature launch and data collection

System Design: From AI Prototype to Production

Context

What to Cover

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