Organizations are increasingly adopting machine learning solutions for log analysis to replace or enhance traditional threshold alerts, which often alert users too late to prevent issues. Threshold alerts depend on predefined conditions that may not account for all potential problems, making them less effective in dynamic environments. Machine learning log analysis offers a proactive approach by identifying critical events before they occur, reducing the need for exhaustive future scenarios planning. Developing an in-house machine learning solution is often cost-prohibitive, so many companies opt for third-party solutions like Coralogix, which provides advanced expertise and functionality in log and data solutions. Coralogix's machine learning-driven log alerting system uses deep learning to filter out noise and prioritize new and critical alerts, thus maintaining system uptime and improving error detection and prevention. This approach not only enhances operational efficiency but also safeguards an organization's reputation and revenue by ensuring high service quality.