Ensemble ML is everywhere - every blog post, every conference talk claims "combine multiple models for better results." But does it actually work in production?I built a data quality monitoring system to find out. Three ML models (Isolation Forest, LSTM, Autoencoder) are working together. 332K synthetic orders processed over 25 days.Here's what actually happened.Why I Tested This"Use ensemble methods" is the standard advice for ML in production. Combine multiple models, get better predictions, and reduce false positives.Sounds great in theory. But I wanted to know:Does it actually reduce false positives?Is the complexity worth it?Does it work for data quality monitoring specifically?So I built it. Ran it continuously. Measured everything.The SetupStack:Apache Kafka streaming ordersPython processing pipelinePostgreSQL for metricsThree ML models in ensembleDocker Compose (runs locally)Data: Synthetic e-commerce orders with realistic quality issues injected.Goal: Compare single model vs. ensemble. Which catches more real issues? Which has fewer false positives?\Baseline: Single Model (Isolation Forest)Started with just Isolation Forest - the standard choice for anomaly detection:\from sklearn.ensemble import IsolationForest# Train on 24 hours of quality metricshistorical_data = get_metrics(hours=24)model = IsolationForest(contamination=0.1)model.fit(historical_data)# Predictis_anomaly = model.predict(current_metrics)Week 1 Results:93% accuracy