D7: ML Operations and Deployment

Process Pillar

ML Operations and Deployment (MLOps) covers the practices for model versioning, automated testing, deployment pipelines, production monitoring, retraining, and rollback. It bridges the gap between experimental model development and reliable production operation.

Why It Matters

The vast majority of AI models never reach production, and those that do often degrade silently without proper monitoring. MLOps provides the operational discipline to deploy models reliably, monitor their performance continuously, detect drift and degradation, and retrain or roll back when necessary — all with full auditability. Without MLOps maturity, organizations cannot scale beyond isolated experiments.

Maturity Levels

Level 1: Foundational

Models are deployed manually with no version control, testing automation, or production monitoring.

Level 2: Developing

Basic CI/CD exists for model deployment with some version tracking, but monitoring is limited to availability rather than model performance.

Level 3: Defined

Standardized MLOps pipelines handle training, validation, deployment, and monitoring with model registries, automated testing, and drift detection.

Level 4: Advanced

Fully automated ML pipelines support continuous training, A/B testing, canary deployments, and automated rollback with comprehensive observability.

Level 5: Transformational

Self-healing ML systems detect and respond to performance degradation autonomously; platform teams enable zero-friction deployment for data science teams.

Key Activities

• Implement a model registry with versioning, metadata, and lineage tracking
• Build automated ML pipelines covering training, validation, and deployment
• Deploy model monitoring for performance metrics, data drift, and concept drift
• Establish rollback procedures and canary deployment strategies
• Create standardized model documentation templates (model cards)
• Define SLAs for model performance, latency, and availability

Assessment Criteria

• Percentage of production models managed through standardized MLOps pipelines
• Availability of automated model monitoring covering drift, performance, and fairness metrics
• Mean time to deploy a new model version from validation to production
• Evidence of automated rollback or alerting when model performance degrades

Abdelalim, T. (2025). “ML Operations and Deployment — COMPEL Process Pillar.” COMPEL by FlowRidge. https://www.compel.one/domain/ml-operations-and-deployment