Design CI/CD for AI Services
Company: Apple
Role: Machine Learning Engineer
Category: System Design
Difficulty: medium
Interview Round: Onsite
Design a CI/CD platform for a large AI product organization. The platform must support backend services, model-serving APIs, data and feature pipelines, and Kubernetes-based infrastructure.
Assume the users are software engineers, machine learning engineers, researchers, and SREs. The organization has hundreds of services, many daily commits, multiple environments, and some GPU-backed deployments.
Your design should cover:
- Source-code integration and pipeline triggering.
- Build, unit test, integration test, security scan, and artifact publishing.
- Model artifact versioning and reproducible deployments.
- Deployment to development, staging, and production Kubernetes clusters.
- Canary, blue-green, or progressive rollout strategies.
- Automatic and manual rollback.
- Secrets, RBAC, policy enforcement, and audit logs.
- Observability for builds, deployments, service health, and model health.
- Handling flaky tests, failed deployments, queue backlogs, and cluster capacity limits.
Provide the high-level architecture, core components, data model, request flow, scaling strategy, reliability plan, and trade-offs.
Quick Answer: This question evaluates the ability to design a scalable CI/CD and MLOps platform that integrates backend services, model-serving APIs, data and feature pipelines, Kubernetes-based deployments, artifact and model versioning, security, and observability.