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.