Complete MLOps Pipeline with Model Training, Versioning, and Deployment

Design a complete MLOps pipeline for [PROJECT NAME], which trains and deploys [TYPE: classification/regression/NLP/recommendation] models in production.\\\\\\\\n\\\\\\\\n**Context:**\\\\\\\\n- Current model: [ALGORITHM/FRAMEWORK: scikit-learn/PyTorch/XGBoost]\\\\\\\\n- Retraining frequency: [DAILY/WEEKLY/MONTHLY]\\\\\\\\n- Data: [VOLUME] records, [NUMBER] features, source [DATABASE/S3/API]\\\\\\\\n- Infrastructure: [AWS/GCP/ON-PREMISE]\\\\\\\\n- Team: [NUMBER] data scientists + [NUMBER] ML engineers\\\\\\\\n\\\\\\\\n**1) Feature Store:**\\\\\\\\n- Feature definitions with metadata (type, range, description)\\\\\\\\n- Feature computation pipeline (batch + real-time)\\\\\\\\n- Tools: [FEAST/TECTON/CUSTOM]\\\\\\\\n- Feature versioning\\\\\\\\n- Data validation: Great Expectations or Pandera\\\\\\\\n- Data drift detection (training vs. serving distribution)\\\\\\\\n\\\\\\\\n**2) Automated Training:**\\\\\\\\n- Training pipeline orchestrated with [AIRFLOW/PREFECT/KUBEFLOW]\\\\\\\\n- Steps: data ingestion → preprocessing → feature engineering → training → evaluation\\\\\\\\n- Hyperparameter tuning: [OPTUNA/RAY TUNE] with compute budget\\\\\\\\n- Experiment tracking: [MLFLOW/WANDB/NEPTUNE]\\\\\\\\n  - Log: params, metrics, artifacts, model, dataset version\\\\\\\\n- Cross-validation strategy\\\\\\\\n- Automatic baseline comparison (new model vs. production model)\\\\\\\\n\\\\\\\\n**3) Model Registry:**\\\\\\\\n- [MLFLOW MODEL REGISTRY/SAGEMAKER/VERTEX AI]\\\\\\\\n- Stages: Development → Staging → Production → Archived\\\\\\\\n- Metadata: metrics, dataset version, training params, creator\\\\\\\\n- Approval workflow: staging → production promotion requires approval\\\\\\\\n- Automatic rollback if metrics degrade\\\\\\\\n- Model card with documentation (bias, limitations, intended use)\\\\\\\\n\\\\\\\\n**4) Serving and Deployment:**\\\\\\\\n- Online serving: [FASTAPI/SAGEMAKER ENDPOINT/SELDON/TRITON]\\\\\\\\n- Batch prediction: scheduled pipeline for mass scoring\\\\\\\\n- A/B testing: shadow mode (new model receives traffic, doesn't decide)\\\\\\\\n- Canary deployment: 5% → 25% → 50% → 100%\\\\\\\\n- Target latency: < [VALUE]ms for p99\\\\\\\\n- Auto-scaling based on request volume\\\\\\\\n\\\\\\\\n**5) Production Monitoring:**\\\\\\\\n- Data drift detection (input features vs. training distribution)\\\\\\\\n- Concept drift detection (performance degradation over time)\\\\\\\\n- Prediction monitoring: output distribution, anomalies\\\\\\\\n- Alerts: automatic retrain trigger when drift > threshold\\\\\\\\n- Dashboard: model performance, latency, throughput, cost\\\\\\\\n\\\\\\\\n**6) CI/CD for ML:**\\\\\\\\n- GitHub Actions/GitLab CI:\\\\\\\\n  - Code push → unit tests → integration tests → training pipeline\\\\\\\\n  - Merge to main → retrain → evaluate → deploy if metrics > threshold\\\\\\\\n- Tests: feature unit tests, pipeline integration tests, model validation tests\\\\\\\\n- Infrastructure as Code for training environments\\\\\\\\n\\\\\\\\nProvide the architecture diagram and Python code for the main components.