Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
This project helps data scientists and analysts automatically build and optimize machine learning models for anomaly detection in IoT data. It takes raw IoT sensor data and, through automated preprocessing, feature engineering, and model selection, outputs highly optimized machine learning models ready for deployment. This is ideal for those managing IoT systems, needing to identify unusual behavior or threats without extensive manual machine learning tuning.
628 stars. No commits in the last 6 months.
Use this if you need to quickly develop and optimize machine learning models for detecting anomalies or patterns in static or continuously streaming IoT data.
Not ideal if your primary goal is general machine learning research on novel algorithms, as this focuses on automating existing, established methods.
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628
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110
Language
Jupyter Notebook
License
MIT
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Last pushed
May 14, 2024
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