MLOps Maturity Model: From Jupyter Notebooks to Fully Automated ML Pipelines
Google defined 3 levels of MLOps maturity. Most organizations are stuck at Level 0.
1. MLOps Maturity Levels
| Level | Description | Characteristics |
|---|---|---|
| Level 0 | Manual Process | Jupyter notebooks, manual deployment, no monitoring |
| Level 1 | ML Pipeline Automation | Automated training, experiment tracking, model registry |
| Level 2 | CI/CD for ML | Automated testing, A/B deployment, continuous monitoring, auto-retraining |
2. Level 2 Pipeline Architecture
# .github/workflows/ml-pipeline.yml
name: ML CI/CD Pipeline
on:
push:
paths: ["training/**", "features/**"]
jobs:
validate-data:
runs-on: ubuntu-latest
steps:
- run: python scripts/validate_data_schema.py
- run: python scripts/check_data_drift.py
train-model:
needs: validate-data
runs-on: [self-hosted, gpu]
steps:
- run: python training/train.py --experiment-name=ci-run-$GITHUB_SHA
- run: python scripts/evaluate_model.py --min-accuracy=0.92
deploy-canary:
needs: train-model
steps:
- run: kubectl set image deployment/model-server model=registry/model:$GITHUB_SHA
- run: python scripts/canary_test.py --traffic-percent=10 --duration=1h
promote-to-production:
needs: deploy-canary
if: success()
steps:
- run: kubectl scale deployment/model-server-canary --replicas=0
- run: kubectl set image deployment/model-server-prod model=registry/model:$GITHUB_SHA
MLOps maturity is the difference between ML as an experiment and ML as a reliable production system.
