Enterprise MLOps Pipeline

System Architecture

A comprehensive MLOps pipeline that combines automated training, deployment, monitoring, and model lifecycle management.

Core Components

1. Model Training Pipeline

class TrainingPipeline:
    def __init__(self, config: Dict[str, Any]):
        self.data_validator = DataValidator(config['validation_config'])
        self.feature_store = FeatureStore(config['feature_config'])
        self.experiment_tracker = ExperimentTracker(
            config['tracking_config']
        )
        self.model_registry = ModelRegistry(config['registry_config'])
        
    async def train_model(
        self,
        model_config: ModelConfig,
        data_config: DataConfig
    ) -> TrainingResult:
        # Validate training data
        validation_result = await self.data_validator.validate(
            data_config
        )
        
        if not validation_result.is_valid:
            raise DataValidationError(validation_result.errors)
            
        # Get features from feature store
        features = await self.feature_store.get_training_features(
            data_config.feature_specs
        )
        
        # Start experiment tracking
        with self.experiment_tracker.start_run() as run:
            # Train model
            model = await self._train_model(
                features,
                model_config
            )
            
            # Evaluate model
            metrics = await self._evaluate_model(
                model,
                features.validation_set
            )
            
            # Register model if metrics meet criteria
            if self._should_register(metrics):
                model_info = await self.model_registry.register_model(
                    model,
                    metrics,
                    model_config
                )
                
        return TrainingResult(
            model_info=model_info,
            metrics=metrics,
            run_id=run.id
        )

2. Model Deployment Pipeline

class DeploymentPipeline:
    def __init__(self, config: Dict[str, Any]):
        self.model_registry = ModelRegistry(config['registry_config'])
        self.deployer = ModelDeployer(config['deployer_config'])
        self.validator = DeploymentValidator(
            config['validation_config']
        )
        self.monitor = DeploymentMonitor(config['monitor_config'])
        
    async def deploy_model(
        self,
        model_id: str,
        deployment_config: DeploymentConfig
    ) -> DeploymentResult:
        # Get model from registry
        model = await self.model_registry.get_model(model_id)
        
        # Validate deployment
        validation_result = await self.validator.validate(
            model,
            deployment_config
        )
        
        if validation_result.is_valid:
            # Deploy model
            deployment = await self.deployer.deploy(
                model,
                deployment_config
            )
            
            # Setup monitoring
            await self.monitor.setup_monitoring(
                deployment,
                deployment_config.monitoring_specs
            )
            
            return DeploymentResult(
                status='success',
                deployment_id=deployment.id,
                endpoints=deployment.endpoints
            )
            
        return DeploymentResult(
            status='failed',
            errors=validation_result.errors
        )

class ModelDeployer:
    def __init__(self, config: Dict[str, Any]):
        self.kubernetes_client = K8sClient(config['k8s_config'])
        self.service_mesh = ServiceMesh(config['mesh_config'])
        self.scaler = AutoScaler(config['scaling_config'])

3. Model Monitoring System

class ModelMonitor:
    def __init__(self, config: Dict[str, Any]):
        self.metric_collector = MetricCollector(
            config['metrics_config']
        )
        self.drift_detector = DriftDetector(config['drift_config'])
        self.performance_analyzer = PerformanceAnalyzer(
            config['performance_config']
        )
        self.alert_manager = AlertManager(config['alert_config'])
        
    async def monitor_model(
        self,
        deployment_id: str
    ) -> MonitoringResult:
        # Collect metrics
        metrics = await self.metric_collector.collect_metrics(
            deployment_id
        )
        
        # Check for drift
        drift_result = await self.drift_detector.detect_drift(
            metrics
        )
        
        # Analyze performance
        performance = await self.performance_analyzer.analyze(
            metrics
        )
        
        # Handle alerts
        if self._should_alert(drift_result, performance):
            await self.alert_manager.send_alerts(
                deployment_id,
                drift_result,
                performance
            )
            
        return MonitoringResult(
            metrics=metrics,
            drift=drift_result,
            performance=performance
        )

class DriftDetector:
    def __init__(self, config: Dict[str, Any]):
        self.feature_drift = FeatureDriftDetector(
            config['feature_config']
        )
        self.prediction_drift = PredictionDriftDetector(
            config['prediction_config']
        )
        self.concept_drift = ConceptDriftDetector(
            config['concept_config']
        )

4. Model Lifecycle Manager

class ModelLifecycleManager:
    def __init__(self, config: Dict[str, Any]):
        self.version_controller = VersionController(
            config['version_config']
        )
        self.artifact_store = ArtifactStore(
            config['artifact_config']
        )
        self.dependency_tracker = DependencyTracker(
            config['dependency_config']
        )
        
    async def manage_lifecycle(
        self,
        model_id: str,
        action: str
    ) -> LifecycleResult:
        # Get model metadata
        model_info = await self.version_controller.get_model_info(
            model_id
        )
        
        if action == 'rollback':
            result = await self._handle_rollback(model_info)
        elif action == 'archive':
            result = await self._handle_archive(model_info)
        elif action == 'update':
            result = await self._handle_update(model_info)
            
        # Update dependencies
        await self.dependency_tracker.update_dependencies(
            model_id,
            result
        )
        
        return result

Usage Example

# Initialize MLOps pipeline
config = {
    'training_config': {
        'experiment_tracking': {
            'tracking_uri': 'mlflow://localhost:5000',
            'registry_uri': 'postgresql://registry-db'
        }
    },
    'deployment_config': {
        'kubernetes_config': {
            'cluster': 'production',
            'namespace': 'ml-models'
        },
        'monitoring_config': {
            'metrics_store': 'prometheus',
            'alert_channels': ['slack', 'email']
        }
    }
}

mlops = MLOpsPipeline(config)

# Train new model
model_config = ModelConfig(
    name='fraud_detection',
    algorithm='xgboost',
    hyperparameters={'max_depth': 6, 'eta': 0.1}
)

training_result = await mlops.train_model(
    model_config,
    data_config
)

# Deploy model
deployment_config = DeploymentConfig(
    replicas=3,
    resources={'cpu': '2', 'memory': '4Gi'},
    monitoring_specs={
        'metrics': ['latency', 'throughput', 'accuracy'],
        'drift_detection': {
            'feature_drift': True,
            'concept_drift': True
        }
    }
)

deployment = await mlops.deploy_model(
    training_result.model_id,
    deployment_config
)

# Monitor deployment
monitoring_result = await mlops.monitor_model(
    deployment.deployment_id
)

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