Cloud environments and open-source software have simplified the implementation of scalable software solutions, yet they have also complicated the task of monitoring due to increasing complexity and data volume. As traditional monitoring struggles to keep pace with these challenges, machine learning-powered log analytics offers a promising solution by automatically identifying correlations between system components, recognizing normal behavior ranges, and alerting on anomalies. This approach addresses the limitations of human oversight in detecting complex relationships and small but critical changes, ultimately enhancing the effectiveness of system monitoring in complex software environments.