Containerized AutoML Services in Life Sciences for Omnichannel Analytics
DOI:
https://doi.org/10.22399/ijcesen.4365Keywords:
AutoML, Containerization, Life Sciences, Biomedical Analytics, Docker, KubernetesAbstract
Containerized Automated Machine Learning (AutoML) services are transforming omnichannel analytics in life sciences by enabling scalable, reproducible, and interoperable machine learning pipelines that unify data from diverse biomedical and operational sources. This review examines how containerization, through technologies such as Docker for environment encapsulation and Kubernetes for orchestration, supports the deployment of AutoML across distributed data environments, including clinical, genomic, and pharmacological channels. By decoupling model training from infrastructure, containerized AutoML systems facilitate cross-platform consistency and seamless integration of structured and unstructured data streams. Empirical evidence demonstrates that these systems achieve superior scalability, reproducibility, and interpretability compared with traditional monolithic approaches. Persistent challenges remain, particularly in ensuring domain-specific interpretability, safeguarding patient privacy, and achieving regulatory-grade interoperability. The review concludes with future research directions aimed at advancing adaptability, transparency, and regulatory compliance for omnichannel life-science analytics.
References
[1] Merkel, D. (2014). Docker: lightweight Linux containers for consistent development and deployment. Linux j, 239(2), 2.
[2] Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y., Chen, R., ... & Snyder, M. (2012). Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell, 148(6), 1293-1307.
[3] Boettiger, C. (2015). An introduction to Docker for reproducible research. ACM SIGOPS Operating Systems Review, 49(1), 71-79.
[4] Hutter, F., Kotthoff, L., & Vanschoren, J. (2019). Automated machine learning: methods, systems, challenges (p. 219). Springer Nature.
[5] Dritsas, E., & Trigka, M. (2025). A survey on the applications of cloud computing in the industrial internet of things. Big data and cognitive computing, 9(2), 44.
[6] Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2017). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589-1604.
[7] Bifet, A., Gavalda, R., Holmes, G., & Pfahringer, B. (2023). Machine learning for data streams: with practical examples in MOA. MIT Press.
[8] Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley interdisciplinary reviews: data mining and knowledge discovery, 9(4), e1312.
[9] Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PloS one, 12(5), e0177459.
[10] Elangovan, K., Lim, G., & Ting, D. (2024). A comparative study of an on-premises AutoML solution for medical image classification. Scientific Reports, 14(1), 10483.
[11] Raghavan, V. A. (2023). Security Threats from Data Poisoning Attacks in Deep Learning Vision Systems (Doctoral dissertation, The George Washington University).
[12] During A. D. (2023). Federated Learning and Privacy-Preserving AI: Revolutionizing AutoML for Real-Time Distributed Database Solutions.
[13] Bhagawati, M., Dhar, C., Sarma, D., Das, M., & Datta, B. K. (2024). Integration of artificial intelligence toward better agricultural sustainability.
[14] Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., ... & Stoica, I. (2016). Apache Spark: a unified engine for big data processing. Communications of the ACM, 59(11), 56-65.
[15] Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). {TensorFlow}: a system for {Large-Scale} machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265-283).
[16] Mandl, K. D., Mandel, J. C., & Kohane, I. S. (2015). Driving innovation in health systems through an apps-based information economy. Cell systems, 1(1), 8-13.
[17] Kumar, P. (2024). AI-Powered Fraud Prevention in Digital Payment Ecosystems: Leveraging Machine Learning for Real-Time Anomaly Detection and Risk Mitigation. Journal of Information Systems Engineering and Management 2024, 9(4) e-ISSN: 2468-4376
[18] Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, omega, and kubernetes. Communications of the ACM, 59(5), 50-57.
[19] Vanschoren, J. (2018). Meta-learning: A survey. arXiv preprint arXiv:1810.03548.
[20] Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain?. arXiv preprint arXiv:1712.09923.
[21] Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19(6), 1236-1246.
[22] Pletzl, S., Haberl, A., Ross-Hellauer, T., & Thalmann, S. (2024). Reproducible automl: An assessment of research reproducibility of no-code automl tools.
[23] Paladino, L. M., Hughes, A., Perera, A., Topsakal, O., & Akinci, T. C. (2023). Evaluating the performance of automated machine learning (AutoML) tools for heart disease diagnosis and prediction. Ai, 4(4), 1036-1058.
[24] Nothaft, F. A. (2017). Scalable systems and algorithms for genomic variant analysis (Doctoral dissertation, UC Berkeley).
[25] Bhattacharjee, A., Barve, Y., Khare, S., Bao, S., Kang, Z., Gokhale, A., & Damiano, T. (2021). Toward Rapid Development and Deployment of Machine Learning Pipelines across Cloud-Edge. In Deep Learning for Internet of Things Infrastructure (pp. 99-128). CRC Press.
[26] Molla, G., & Bitew, M. (2024). Revolutionizing personalized medicine: synergy with multi-omics data generation, main hurdles, and future perspectives. Biomedicines, 12(12), 2750.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
