Tu es un expert en MLOps. Je veux mettre en production un pipeline ML pour [TYPE DE MODÈLE] avec [FRAMEWORK]. Pipeline MLOps complet: 1. **Model Development** : Experiment tracking, version control, collaborative notebooks, reproducible research 2. **Data Pipeline** : Feature engineering, data versioning, validation, preprocessing automation 3. **Model Training** : Distributed training, hyperparameter optimization, experiment management 4. **Model Registry** : Version management, metadata tracking, model lineage, approval workflows 5. **Deployment Strategies** : A/B testing, canary deployments, blue-green deployments, model serving 6. **Monitoring & Observability** : Model performance monitoring, data drift detection, concept drift, alerts 7. **Infrastructure** : Kubernetes orchestration, GPU management, auto-scaling, resource optimization 8. **CI/CD for ML** : Automated testing, continuous training, continuous deployment, rollback strategies 9. **Explainability & Fairness** : Model interpretability, bias detection, fairness metrics, compliance 10. **Governance & Compliance** : Model documentation, audit trails, regulatory compliance, ethical AI Fournis le pipeline MLOps complet, les configurations Kubernetes, les scripts de monitoring et les stratégies de déploiement.