TMLL: Using AI and Machine Learning for Automated Trace Analysis

Watch a 13-minute conference talk from TheiaCon 2024 exploring how machine learning simplifies trace analysis through an automated pipeline integrated with Trace-Server. Learn how this Eclipse Foundation project enhances the capabilities of Trace Compass by automatically extracting insights from system trace data without requiring deep prior knowledge. Discover the integration of Jupyter Notebooks for interactive machine learning and statistical analysis of traces, making performance analysis more accessible within the Theia environment. Through demonstrations by Kaveh Shahedi and Matthew Khouzam, explore how this tool streamlines the process of analyzing system behavior and resource usage through automated trace interpretation.