Evaluating machine learning models in cloud-based platforms requires a comprehensive set of performance and operational metrics to ensure both predictive quality and efficient deployment. For classification tasks, essential metrics include Accuracy, Precision, Recall, and the F1-score, often complemented by a Confusion Matrix and ROC AUC curve to assess class separability and true positive/negative rates. Regression models, conversely, are typically evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, which quantify the deviation between predictions and actual values. In cloud environments, operational metrics become equally critical, encompassing inference latency, model throughput, resource utilization, and overall computational cost to ensure scalability and cost-effectiveness. Furthermore, assessing model fairness and its business impact are increasingly vital for responsible and effective real-world deployments. Ultimately, the choice of metrics depends on the specific problem, model type, and the deployment's business objectives within the cloud infrastructure. More details: https://www.manevihayat.com/proxy.php?link=https://4mama.com.ua/