Four levels of ML operationalization - from ad-hoc notebooks to fully automated pipelines.
Level 0 - Manual ML
Everything done by hand. Data scientists run notebooks locally. No reproducibility, no monitoring.
Level 1 - ML Pipeline
Reusable training pipeline. Experiments tracked. Manual deployment trigger. Basic serving.
Level 2 - CI/CD ML
Code commit triggers training + evaluation. Automated deployment on quality gate pass.
Level 3 - Full MLOps
Data drift triggers retraining. Canary deployments. Full audit trail. Self-healing pipelines.
Area
L0
L1
L2
L3
Data Prep
Manual scripts, no versioning
Automated pipeline, DVC versioning
NEXT: Validated, schema-checked
Self-healing, drift-aware
Training
Notebook, manual run
Parameterized pipeline
NEXT: Auto-triggered on code commit
Auto-triggered on data drift
Evaluation
Manual spot-check
Automated metrics logging
NEXT: Multi-metric quality gates
Champion/challenger testing
Serving
Batch script / ad-hoc
REST API, manual deploy
NEXT: CI/CD automated deploy
Canary + shadow deployment
Monitoring
None
Basic accuracy alerts
NEXT: Drift + latency dashboards
Automated retraining triggers
Reproducibility
None
Experiment tracking (MLflow)
NEXT: Full lineage tracked
End-to-end audit trail
Maturity Progress - 33% to Full MLOps
L0
L1
L2
L3
Current Level
Gap Analysis
To reach Level 2 - CI/CD ML:
• Data Prep: Validated, schema-checked
• Training: Auto-triggered on code commit
• Evaluation: Multi-metric quality gates
• Serving: CI/CD automated deploy
• Monitoring: Drift + latency dashboards
• Reproducibility: Full lineage tracked
Key Insight
Most teams skip levels. Going from L0 → L3 directly fails. Each level builds on the previous - you need working pipelines before you can automate them.
MLOps Maturity Model - Interactive Visualization
The MLOps Maturity Model describes four levels of ML operationalization. Level 0 is fully manual - data scientists run notebooks locally with no versioning or reproducibility. Level 1 introduces reusable pipelines and experiment tracking. Level 2 adds CI/CD so code commits automatically trigger training and deployment. Level 3 achieves full MLOps where data drift itself triggers retraining and deployments are fully automated.
Level 0: Manual ML - notebooks, no versioning, no monitoring, no reproducibility
Level 1: ML Pipeline - parameterized training, experiment tracking with MLflow, REST API serving
Level 2: CI/CD ML - code commits trigger automated training, evaluation gates, and deployment
Level 3: Full MLOps - data drift triggers retraining, canary deployments, self-healing pipelines
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