Open AccessLow-code AutoML-augmented Data Pipeline – A Review and Experiments
Author(s) -
Ulla Gain,
Virpi Hotti
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012015
Subject(s) - pipeline (software) , computer science , code (set theory) , raw data , adaptability , data science , software , software engineering , data mining , programming language , ecology , set (abstract data type) , biology
There is a lack of knowledge concerning the low-code autoML (automated machine learning) frameworks that can be used to enrich data for several purposes concerning either data engineering or software engineering. In this paper, 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content. The PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory (Colab) and the BI (business intelligence) tool. Finally, the low-code autoML-augmented data pipeline from raw data to dashboards and low-code apps has been drawn based on the experiments concerned classifications of the “Census Income” dataset. The constructed pipeline preferred the same data to be a ground for different reports, dashboards, and applications. However, the constructed low-code autoML-augmented data pipeline contains changeable building blocks such as libraries and visualisations.