TMLL Trace Server Machine Learning Library, Use AI for Trace Analysis
summary
TMLL sits between Trace-Server and analysis code, ingesting Trace Compass outputs and running automated pipelines that produce anomaly windows, seasonality plots, correlation matrices and predictive-maintenance scores. It implements statistical and ML detectors (z‑score, IQR, moving averages, isolation forest), reduces thousands of raw events to tens of summary states, exposes Jupyter-friendly outputs for Theia and PyTorch, and handles large traces by using a sliding window to avoid multi‑gigabyte transfers.
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chapters (11)
- 0:00Introduction
- 0:41Trace Compass and tracing
- 2:04Trace Server overview
- 3:27User challenges with Trace Server
- 4:52TMLL introduction
- 5:36Anomaly detection with TMLL
- 6:42Seasonality analysis
- 8:49Correlation analysis in TMLL
- 10:13TMLL library usage
- 10:54Summary
- 11:16Q&A
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