MLOPS-MATURITY-MODEL
MLOps Maturity Model 0-4 levels Microsoft Azure framework.
Définition
MLOps Maturity Model Microsoft Azure 5 levels detail : (1) Level 0 - No MLOps : Manual builds + deployments, no version control, no automated tests, no monitoring, training + deployment activities done on data scientists laptops, scripts + notebooks shared informally, single team data science + ML engineering. (2) Level 1 - DevOps but no MLOps : Automated builds + tests + deployments of application code (via Azure DevOps + Jenkins + GitHub Actions etc.), but model training + deployment still manual, model versioning unclear, separation app code DevOps + model lifecycle. (3) Level 2 - Automated Training : ML training pipelines automated (Azure ML Pipelines + Kubeflow Pipelines + Vertex AI Pipelines), model training reproducible + version-controlled, automated tests model quality. (4) Level 3 - Automated Model Deployment : models packaged + deployed via CI/CD (containerized + orchestrated), A/B testing + canary deployments + rollback capabilities, model registry + model versions + stages (staging + production). (5) Level 4 - Full MLOps Automated Operations : retraining triggered automatically (schedule + drift detection + performance degradation), model monitoring (drift detection input + output + performance + business metrics), alerting integrated incident management, full lineage tracking data + experiments + models + deployments + business outcomes. Adoption : ~30% enterprises Level 0-1, ~40% Level 2, ~25% Level 3, ~5% Level 4 (2024 estimates).
Origine
Microsoft Azure MLOps Maturity Model publie 2020 par Microsoft Architecture Center ; Google MLOps Maturity Model similar (Google Cloud version 3 levels) ; AWS MLOps Maturity Model similar.
Exemple en contexte
Enterprise insurance company self-assesses MLOps Maturity Model : actuellement Level 2 (automated training pipelines via Azure ML), roadmap 2025-2026 transition Level 3 (automated model deployment + A/B testing + rollback) puis Level 4 (full automated retraining + drift monitoring) ; investment ~$5M MLOps platform + team expansion ~10 ML engineers.
Termes liés
- MLflow — MLOps tool.