pyod
loglizer
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| pyod | loglizer | |
|---|---|---|
| 7 | 2 | |
| 7,928 | 1,225 | |
| - | 2.0% | |
| 7.7 | 1.8 | |
| 21 days ago | 5 months ago | |
| Python | Jupyter Notebook | |
| BSD 2-clause "Simplified" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyod
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A Comprehensive Guide for Building Rag-Based LLM Applications
This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod
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Analyze defects and errors in the created images
PyOD
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Multivariate Outlier Detection in Python
Check out the algorithms and documentation in this toolkit. It’ll give you a list of methods to read up on to understand their mechanisms. https://github.com/yzhao062/pyod
- Pyod – A Comprehensive and Scalable Python Library for Outlier Detection
- Predictive Maintenance and Anomaly Detection Resources
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[D] Unsupervised Outlier Detection - Advise Requested
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
- PyOD: ~50 anomaly detection algorithms in one framework.
loglizer
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SOC with machine learning
https://github.com/logpai/loglizer has an MIT license. Seems like they've done some of the heavy lifting already. If you're just looking for logs, check out https://github.com/logpai/loghub.
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how to never ever lose connection to raspberry pi
If you want to really get paranoid, then you can write a monitoring app that uses machine learning to do the log analysis and detect anomalies in your system. There are some open source tools available, like this for example. Also you can train the network for your specific use case and then just have the service running the inference on your logs and a pre-trainer model that is running on system logs. Then you really get in paranoid mode.
What are some alternatives?
tods - TODS: An Automated Time-series Outlier Detection System
Fog - An open source computer cloning & management system
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
wazuh-ruleset - Wazuh - Ruleset
alibi-detect - Algorithms for outlier, adversarial and drift detection
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
pycaret - An open-source, low-code machine learning library in Python
luminol - Anomaly Detection and Correlation library
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis
kafkaml-anomaly-detection - Project for real-time anomaly detection using Kafka and python
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
HELK - The Hunting ELK