CI/CD for ML Models: From Training Notebooks to Production Deployment in 2026

CI/CD for ML Models: From Training Notebooks to Production Deployment in 2026 Deploying ML models isn't like deploying web apps. Models have data dependencies, training artifacts, hardware requirements, and failure modes that traditional CI/CD pipelines don't handle. Most teams either deploy models manually (risky) or bolt ML onto web app pipelines (broken). This guide shows how to build ML-specific CI/CD pipelines that actually work. Why Standard CI/CD Breaks for ML Web App Pipeline: ML Pipeline: Code → Build → Test → Code → Build → Test → Deploy → Monitor Train → Validate → Package → Deploy → Monitor → Retrain ML adds three challenges: Artifacts are large — models are 100MB-100GB, not 10MB Docker images Tests need data — you can't unit test a model without test datasets Rollbacks need both code AND model — deploying old code with new model (or vice versa) breaks things The Pipeline Architecture # .github/workflows/ml-pipeline.yml name : ML Model Pipeline on : push : paths : - ' model