ML System Design: From Research to Production

Module: Machine Learning | Difficulty: Advanced

ML System Components

1. Data Pipeline: Ingestion, validation, preprocessing
2. Feature Store: Online/offline features, feature computation
3. Training Pipeline: Distributed training, hyperparameter tuning
4. Model Serving: Low-latency inference, A/B testing
5. Monitoring: Drift detection, performance metrics

Feature Store

Model Serving Latency

Monitoring

• Data drift:
• Concept drift: changes over time
• Performance: Accuracy, latency, throughput

class MLSystem:
    def __init__(self):
        self.feature_store = FeatureStore()
        self.model = None
        self.monitor = Monitor()
    def predict(self, entity_id):
        features = self.feature_store.get_features(entity_id)
        prediction = self.model.predict(features)
        self.monitor.log_prediction(entity_id, features, prediction)
        return prediction
    def monitor_drift(self, window_size=1000):
        recent = self.monitor.get_recent(window_size)
        drift_score = self.compute_drift(recent)
        if drift_score > self.threshold:
            self.trigger_retraining()
        return drift_score

Research Insight: The key challenge in production ML is maintaining model performance over time. Data drift and concept drift are inevitable, so automated monitoring and retraining pipelines are essential for long-term success.