Automated Machine Learning (AutoML) and Augmented Analytics: Transforming the Future of Data-Driven Decision Making

Abstract

The rapid growth of data in recent years has created the need for intelligent systems that can analyze, interpret and extract knowledge with minimal human effort. Automated Machine Learning (AutoML) and Augmented Analytics are emerging as powerful paradigms that reduce the complexity of traditional data science workflows. AutoML automates model selection, feature engineering, and hyperparameter tuning, while augmented analytics uses artificial intelligence to enhance data preparation, insight generation, and visualization. These technologies allow non-experts to perform advanced analytics and reduce dependency on skilled data scientists. This paper reviews the concepts, architecture, tools, benefits, challenges, and real-world applications of AutoML and augmented analytics. It also discusses how these technologies are transforming business intelligence, healthcare, finance, and cybersecurity domains. Some limitations and ethical concerns are also addressed. The integration of AutoML and augmented analytics is shaping a future where datadriven decision making becomes faster, more accurate, and accessible to all.

KEYWORDS: AutoML, Augmented Analytics, Data Science Automation, Machine Learning, Intelligent Analytics, Hyperparameter Tuning, AI in Analytics