Monitor ML models by tracking performance metrics and system characteristics for optimal performance, reliability, and scalability. Employ data monitoring, model evaluation, and resource tracking. Analyze data quality, distribution, and drift; track metrics like accuracy, F1-score, and AUC-ROC for classification or MSE, MAE, and R-squared for regression. Assess resource utilization by monitoring GPU/CPU usage, memory, and latency. Use tools like Aporia, TensorBoard, MLflow, or Prometheus to streamline monitoring and ensure stability, accuracy, and efficiency.