Abstract
The increasing volume and complexity of data in modern enterprises demand automated approaches to extract actionable insights efficiently. Autonomous data mining pipelines, combining Automated Machine Learning (AutoML) and Automated Knowledge Discovery in Databases (AutoKDD), represent a paradigm shift in handling end-to-end data workflows with minimal human intervention. This paper reviews the state-of-the-art methodologies in AutoML and AutoKDD, highlights their integration into autonomous pipelines, and evaluates their capabilities, limitations, and applications across industries. Key challenges such as model interpretability, scalability, and ethical considerations are discussed. The paper also provides a comparative analysis of prominent tools and frameworks, emphasizing best practices for pipeline design and deployment.
Keywords: AutoML, AutoKDD, Autonomous Data Mining, Machine Learning Pipelines, Knowledge Discovery, Data Analytics, Automated Model Selection
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