The rapid growth of AutoML platforms has enabled business users and data scientists to accelerate machine learning workflows, particularly for tabular prediction tasks. This research paper presents a benchmark comparison of Qlik Predict and DataRobot, two leading AutoML systems, across three fundamental machine learning problems: regression, binary classification, and multiclass classification. Using large, real-world datasets; including the YouTube Shorts & TikTok Trends 2025, Forest Cover Type, and Wisconsin Breast Cancer Diagnostic datasets. The study measures out-of-sample accuracy, computational efficiency, and practical usability. Experiments were conducted with standardized resources, with DataRobot processes allocated six CPU cores. Qlik Predict consistently demonstrated equal or superior accuracy with drastically reduced training time. These findings confirm its effectiveness and scalability for high-stakes business and research applications, contributing valuable evidence for AutoML selection and enterprise analytics strategies.
