MLOps 2.0: A Reference Architecture for CI/CD with Always-On Data Quality Gates

MLOps 2.0 operationalizes machine learning by elevating data to a first-class artifact alongside code and models. We present a reference architecture that converges CI/CD with Continuous Data Validation (CDV), inserting data quality gates schema, semantic, temporal, and distributional across build, test, release, and run stages. The pipeline encodes data contracts, enforces SLO-aligned promotion criteria, and couples training/inference observability to reduce escaped defects (silent drift, schema skew, target leakage) that CI/CD alone cannot catch. A domain-agnostic evaluation template quantifies impacts on lead time, rollback rate, stability, and incident frequency. Results indicate CI/CD +CDV yields more reliable, auditable, and cost-efficient ML delivery.