MLOps 2.0: A Reference Architecture for CI/CD with Always-On Data Quality Gates
Abstract
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 SLOaligned 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.
Fields of Study & Subjects
Advanced Data Storage Technologies
Advanced Database Systems and Queries
Data Quality and Management
Publication details
- Journal / source
- International Journal of Artificial Intelligence and Machine Learning
- Publisher
- SvedbergOpen
- Volume
- 6
- Issue
- 1
- Pages
- 118–131
- Publication date
- 20 January 2026