Scope
<p><strong>Eclipse Trace Server Machine Learning Library (TMLL) </strong>provides an automated pipeline that applies machine learning techniques to the analyses derived from the trace server. The goal of TMLL is to simplify the process of performing both primitive trace analyses and complementary machine learning-based investigations. </p><p>Eclipse TMLL features:</p><ul><li><strong>Automated Trace Data Analysis:</strong> Provide a streamlined pipeline for analyzing trace data from the trace server using both traditional methods and machine learning techniques.</li><li><strong>Machine Learning Integration:</strong> Incorporate multiple machine learning techniques (supervised, unsupervised, reinforcement learning, etc.) for tasks like anomaly detection, predictive maintenance, and resource optimization.</li><li><strong>Modular and Flexible Design:</strong> Allow users to plug in different modules (e.g., anomaly detection, trend analysis) tailored to specific system performance analysis needs, similar to libraries like <em>PyCaret</em>1.</li><li><strong>User-Friendly API:</strong> Offer a simple, intuitive interface for users with minimal ML or trace analysis expertise, making it easy to apply sophisticated analysis methods programmatically.</li><li><strong>Comprehensive System Insights:</strong> Provide a range of outputs such as performance trends, anomaly alerts, root cause identification, and optimization recommendations to help users manage and improve system performance.</li><li><strong>Extensibility and Customization:</strong> Enable developers and system administrators to extend the library by adding custom analysis modules or integrating with other performance monitoring tools.</li><li><strong>Visualization Capabilities:</strong> Include built-in methods for visualizing trace analysis results, such as heatmaps, time-series plots, or performance trend charts. </li></ul>
