Analysis of modern predictive analytics systems

DOI: 10.31673/2412-9070.2025.027261

Abstract

Abstract: The article presents a comparative analysis of modern predictive analytics systems (PAS) that employ machine learning methods. The study focuses on evaluating the core capabilities, architectural models, and application areas of widely used platforms, including IBM Watson Studio, Google Vertex AI, Microsoft Azure Machine Learning Studio, Amazon SageMaker, RapidMiner, DataRobot, H2O.ai, and SAS Predictive Analytics. Special attention is given to the classification of PAS by the types of tasks they solve (classification, regression, time series forecasting), levels of automation (AutoML, semi-automated, and custom solutions), and deployment models (on-premise, cloud-based, and hybrid).
The analysis highlights key criteria for comparison: architectural flexibility and scalability, support for machine learning algorithms and AutoML features, integration with diverse data sources, data preparation and visualization tools, model performance and accuracy, security compliance (GDPR, ISO), and user experience including interface convenience, documentation, and vendor support.
Each system is assessed in terms of its strengths and weaknesses, depending on its suitability for various use cases. Enterprise-level platforms such as IBM Watson Studio, SAS, and Azure ML Studio are identified as optimal for regulated environments and large-scale deployments. In contrast, cloud native solutions like Google Vertex AI and Amazon SageMaker demonstrate high flexibility and DevOps integration, making them ideal for scalable AI projects. Tools such as RapidMiner and DataRobot are more suitable for rapid prototyping and business users due to their intuitive interfaces and AutoML capabilities. Open-source platforms like H2O.ai are shown to be effective for research, experimentation, and startups thanks to their performance, transparency, and community support.
The paper concludes by outlining the current trends in PAS development and identifying promising directions for future research. These include the further integration of explainable AI (XAI), ethical data handling, and the convergence of predictive analytics with real-time data processing in cloud ecosystems. Based on the analysis, practical recommendations are proposed to assist organizations in selecting appropriate PAS tools aligned with their technical needs and strategic objectives.

Keywords: predictive analytics, machine learning, cloud platforms, decision support systems, AutoML.