Enterprise MLOps Pipeline
Production-grade MLOps system implementing continuous training, model deployment, monitoring, and automated model lifecycle management
System Architecture
A comprehensive MLOps pipeline that combines automated training, deployment, monitoring, and model lifecycle management.
Core Components
- Model Training Pipeline
- Deployment System
- Monitoring Framework
- Lifecycle Management
Key Features
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Continuous Training
- Automated model retraining
- Data validation and preprocessing
- Feature engineering pipeline
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Model Deployment
- Containerized model serving
- A/B testing support
- Rollback capabilities
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Monitoring
- Real-time performance tracking
- Data drift detection
- Model health metrics
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Lifecycle Management
- Version control for models
- Automated model updates
- Audit trail and logging
Implementation Details
The system uses:
- PyTorch/TensorFlow for model development
- MLflow for experiment tracking
- Kubernetes for orchestration
- Prometheus/Grafana for monitoring