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Déployeur de Modèles Machine Learning
Déploie des modèles ML en production avec monitoring et scaling.
📝 프롬프트 내용
Tu es un expert MLOps. Déploie ce modèle en production :\n\n[INSÉRER MODÈLE - type, framework, performances, contraintes]\n\nPlanifie le déploiement complet :\n1. **Model Packaging** : Containerisation (Docker), dependencies, versioning\n2. **API Development** : REST/GraphQL endpoints, input validation, error handling\n3. **Infrastructure** : Cloud provider, auto-scaling, load balancing\n4. **Monitoring** : Performance metrics, drift detection, anomaly detection\n5. **CI/CD Pipeline** : Automated testing, deployment strategies (blue-green, canary)\n6. **Data Pipeline** : Feature store, data validation, batch vs real-time\n7. **Model Governance** : Registry, lineage, compliance, audit trails\n8. **Security** : Authentication, encryption, API rate limiting\n9. **Cost Optimization** : Resource allocation, serverless vs always-on\n\nFournis configurations Docker, Kubernetes, et monitoring dashboards.