PyODDS: An End-to-End Outlier Detection System
This system addresses the problem of data movement and accessibility in outlier detection for users with or without machine learning expertise, though it is incremental as it builds on existing algorithms.
The authors tackled the challenge of making outlier detection accessible and efficient by developing PyODDS, an end-to-end Python system with database support that allows in-database execution of algorithms, resulting in a tool that integrates statistical and deep learning approaches for users across various fields.
PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches. PyODDS is released under the MIT open-source license, and currently available at (https://github.com/datamllab/pyodds) with official documentations at (https://pyodds.github.io/).