Challenges in AutoML and Declarative Studies Using Systematic Literature Review

Abstract

Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.