Youzan built an automated offline data cost‑optimization platform that gathers accurate metadata, mines unused or failing tables and tasks, and safely decommissions them through a backend‑frontend workflow with owner validation, notifications, rollback safeguards, and plans to extend lineage coverage and real‑time asset handling.
The article outlines a comprehensive quality‑assurance framework for algorithm models on the Yanxuan platform, detailing lifecycle stages, common issues, and a unified platform that automates bad‑case mining, model‑effect monitoring, latency tracking, and pipeline validation to ensure reliable deployment across search, recommendation, marketing, bidding, and forecasting applications.
This guide details a complete GitLab‑based CI/CD workflow that starts with issue and milestone creation, defines feature‑branch pipelines, builds and tests Java applications, performs code analysis, creates Docker images, and deploys to Kubernetes with controlled pipeline triggers and rollback strategies.
