SmartPipeline: A Production-Grade Automated Machine Learning Platform with Intelligent Pipeline Orchestration

This paper presented SmartPipeline, a comprehensive AutoML platform automating the complete machine learning pipeline from data ingestion through model deployment. Principal contributions include a unified architecture supporting both supervised and unsupervised learning workflows, intelligent preprocessing with automatic strategy selection, multimetric model evaluation with anti-overfitting mechanisms, diversity-aware ensemble construction, and interpretable results with confidence scoring. Experimental evaluation demonstrated competitive predictive performance while substantially reducing development effort. The platform successfully processes diverse file formats and automatically detects appropriate learning paradigms.

[1]     F. Hutter, L. Kotthoff, and J. Vanschoren, Automated Machine Learning: Methods, Systems, Challenges, Springer, 2019. [2]     X. He, K. Zhao, and X. Chu, AutoML: A Survey of the State-of-the-Art, Knowledge-Based Systems, vol. 212, 2021. [3]     M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter, Efficient and Robust Automated Machine Learning, Advances in Neural Information Processing Systems, 2015. [4]     E. LeDell and S. Poirier, H2O AutoML: Scalable Automatic Machine Learning, ICML AutoML Workshop, 2020. [5]     Google Cloud, Cloud AutoML Technical Documentation, 2023. [6]     Microsoft Azure, Azure Machine Learning Automated ML Documentation, 2023. [7]     R. S. Olson and J. H. Moore, TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning, ICML AutoML Workshop, 2016. [8]     J. Snoek, H. Larochelle, and R. P. Adams, Practical Bayesian Optimization of Machine Learning Algorithms, Advances in Neural Information Processing Systems, 2012. [9]     T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, Optuna: A Next-generation Hyperparameter Optimization Framework, International Conference on Knowledge Discovery and Data Mining, 2019. [10]   D. H. Wolpert, Stacked Generalization, Neural Networks, vol. 5, no. 2, pp. 241-259, 1992. [11]   R. E. Schapire, The Strength of Weak Learnability, Machine Learning, vol. 5, pp. 197-227, 1990. [12]   R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes, Ensemble Selection from Libraries of Models, International Conference on Machine Learning, 2004. [13]   Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu, Understanding of Internal Clustering Validation Measures, IEEE International Conference on Data Mining, 2010. [14]   F. Biessmann et al., DataWig: Missing Value Imputation for Tables, Journal of Machine Learning Research, vol. 20, pp. 1-6, 2019. [15]   S. Krishnan, J. Wang, M. J. Franklin, K. Goldberg, and T. Kraska, ActiveClean: Interactive Data Cleaning for Statistical Modeling, International Conference on Very Large Data Bases, 2016.