Low-code AutoML-augmented Data Pipeline – A Review and Experiments

Abstract

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.

Machine learning of microvolt-level 12-lead electrocardiogram can help distinguish takotsubo syndrome and acute anterior myocardial infarction

“…Eleven ML models were built using PyCaret, an open-source wrapper over several ML libraries in Python in a low-code environment. 11 After screening of the 11 models, we adopted 2 excellent ensemble learning methods: an extra tree classifier (model_ET) and a light gradient boosting machine (model_LGBM), 12 , 13 which uses many random decision trees and built a majority vote-like system. A brief explanation of the ensemble learning is provided in Supplemental File 1 .…”

EEG-Based Anamoly Detection for Autistic Kids – A Pilot Study

“…PyCaret is a Python-based machine learning framework for automating machine learning workflows [11,13]. Anomaly detection is the process of discovering unusual things, events, or observations that raise concerns because they differ significantly from the overall set of data points.…”

“…However, when data streams vary over time, standard approaches become ineffective, necessitating the use of an outlier identification algorithm that can handle dynamic data streams. PyCaret is a Python machine learning package that automates machine learning operations and is open-source [13]. Low-code autoML framework PyCaret (automatic machine learning) can be utilized to enhance data for a variety of applications, but there is a dearth of expertise about them.…”

Perbandingan Performa Model Data Mining untuk Prediksi Dropout Mahasiwa

“…PyCaret dapat juga dikustomisasi untuk keperluan tertentu seperti yang dilakukan oleh (do Nascimento et al, 2021) untuk keperluan analisis prediktif pada laboratorium kesehatan. PyCaret merupakan salah satu dari banyak framework autoML lainnya seperti PyTorch, Theano, dan lain sebagainya (Gain & Hotti, 2021). Menurut riset (Gain & Hotti, 2021), PyCaret memiliki keunggulan karena ia kompatibel dengan Google Colaboratory (Colab) dan aplikasi BI (business intelligence).…”

“…PyCaret merupakan salah satu dari banyak framework autoML lainnya seperti PyTorch, Theano, dan lain sebagainya (Gain & Hotti, 2021). Menurut riset (Gain & Hotti, 2021), PyCaret memiliki keunggulan karena ia kompatibel dengan Google Colaboratory (Colab) dan aplikasi BI (business intelligence). PyCaret sendiri memiliki beberapa modul yaitu klasifikasi, regresi, clustering, Association Rule Mining, deteksi anomali, dan Natural Language Processing.…”