End-to-End MLOps Pipeline on Kubernetes for Scalable Healthcare Applications
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I1P108Keywords:
MLOps, Kubernetes, Healthcare Applications, Scalability, Machine Learning, Continuous Integration, Continuous Deployment (CI/CD), Model Deployment, Data Pipelines, Container Orchestration, Fault Tolerance, Predictive Analytics, Kubeflow, ML flow, Airflow, Data Privacy, Regulatory Compliance, Model Monitoring, Automation, Edge ComputingAbstract
One notable impact of the use of machine learning (ML) in the medical field is the increased need for dependable, automated, and scalable workflows for operations, whereby MLOps has become a crucial practice that bridges the gap between model creation and model application. MLOps, through all the stages of the ML model lifecycle from data preprocessing to training, deployment, and monitoring, changes the way this process is done while it also assures all other necessary healthcare-specific requirements, such as compliance with standards, auditability for traceability of any changes, and continuous improvement through further monitoring and feedback loops. All these are exactly the requirements in healthcare, where the accuracy of the model along with its reliability will have a direct impact on patient outcomes. By way of his substantial container orchestration abilities, Kubernetes has come to be the manufacturing facility for scaled as well as fault-free MLOps pipelines, and thus, it offers quite a few automated features, including automated scaling, trouble-free updates, effective resource management, etc., which are the tools to overcome the healthcare applications' changing workloads as well as the applications' nature, such as diagnostics, personalised treatment, and predictive analytics. Kubeflow, ML flow, and Airflow are three open-source technologies that Kubernetes is compatible with. Their association with Kubernetes allows them to build ML pipelines from start to finish that are not only easily restorable from the fault but also can be even more covered with the extent of available training datasets and are well connected with existing ML systems. This paper describes the architecture that takes a healthcare Kubernetes-based MLOps pipeline and faces problems related to data privacy, regulatory compliance, and model interpretability and also presents an example of the advantages of automation, CI, and monitoring practice. The article is hereupon to state the advantages of Kubernetes and, going further, to point at the future in which a number of discussions could be gaining ground, including the one about large language model (LLM) adoption, federated learning, and edge computing, all of them invented with the idea of helping healthcare to meet the demand arising
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