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AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics
This repository provides a tutorial implementation of AutoML for batch and online learning.
About AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics
This open-source GitHub repository provides a tutorial implementation of Automated Machine Learning (AutoML) for IoT data analytics. It covers automated data pre-processing, automated feature engineering, automated model selection, hyperparameter optimization (HPO), and automated model updating to handle concept drift. The README states that it can also be used as a tutorial for machine learning researchers who want to obtain optimized models for a specific task.
The repository is organized around two settings. Batch learning notebooks work on static data that is analyzed in batches, and online learning notebooks work on continuously arriving data streams. The main case study is IoT anomaly detection, and the README names four notebooks, two for each setting. The code accompanies a review paper by L. Yang and A. Shami published in Engineering Applications of Artificial Intelligence in 2022.
Key features
- Automated data pre-processing for static and dynamic IoT data
- Automated feature engineering within the AutoML pipeline
- Automated model selection across common machine learning algorithms
- Hyper-parameter optimization (HPO) for tuning algorithm settings
- Automated model updating to address concept drift in online learning
- Batch and online learning notebooks, each with two datasets
Best for: Machine learning researchers, data analysts, and industrial users who want a worked example of AutoML applied to IoT data.
Good for
- Covers the main AutoML pipeline stages in one repository
- Includes both batch and online learning examples
- Linked to a review paper published in Engineering Applications of Artificial Intelligence
Watch out for
- The main case study is limited to IoT anomaly detection
AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics at a glance
- Category
- Education & Learning › AI Learning & Guides
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Good for
- Researchers
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics FAQ
Does the repository cover both batch and online learning?
Yes. It includes batch learning notebooks for static data and online learning notebooks for continuously incoming data streams.
Is there a related publication?
Yes. The code implements a review paper by L. Yang and A. Shami, titled 'IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective', published in Engineering Applications of Artificial Intelligence in 2022.
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