Prompt

Comprehensive ML model integration and MLOps workflow automation:

**Model Integration & Deployment:**
- Set up ML model serving and inference endpoints
- Implement model versioning and A/B testing
- Create model deployment automation and rollback
- Design model performance monitoring and validation

**Data Pipeline & Feature Engineering:**
- Create automated feature engineering pipelines
- Set up data validation and quality monitoring
- Implement feature store and data versioning
- Design data drift detection and alerting

**Model Training & Experimentation:**
- Set up automated model training workflows
- Implement experiment tracking and model comparison
- Create hyperparameter tuning automation
- Design model validation and testing pipelines

**MLOps Infrastructure:**
- Set up ML workflow orchestration (Kubeflow, MLflow)
- Implement model registry and artifact management
- Create automated model testing and validation
- Design ML pipeline monitoring and observability

**Production ML Monitoring:**
- Implement model performance monitoring in production
- Set up data and model drift detection
- Create automated retraining triggers and workflows
- Design ML system reliability and error handling

**Example MLOps Operations:**
```bash
# Model serving with FastAPI
uvicorn model_server:app --host 0.0.0.0 --port 8000
# ML experiment tracking
mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifacts
# Model deployment
docker build -t ml-model:latest . && docker push ml-model:latest
# Feature pipeline
python feature_pipeline.py --input data/ --output features/
```

Provide comprehensive MLOps workflows with model integration and production monitoring.