Vol. 4 No. 2 (2024): Hong Kong Journal of AI and Medicine
Articles

Exploring the Role of Kubernetes in MLOps and DevOps for Containerized Machine Learning Model Management

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  • John Smith
John Smith
PhD, Senior Research Scientist, Data Science Institute, New York, USA

Published 02-10-2024

Keywords

  • Kubernetes,
  • MLOps
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
J. Smith, “Exploring the Role of Kubernetes in MLOps and DevOps for Containerized Machine Learning Model Management”, Hong Kong J. of AI and Med., vol. 4, no. 2, pp. 88–94, Oct. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://hongkongscipub.com/index.php/hkjaim/article/view/69

Abstract

As the demand for scalable and efficient machine learning (ML) solutions increases, the need for robust operational frameworks becomes crucial. Kubernetes has emerged as a leading platform for managing containerized applications, providing the necessary tools for deploying, scaling, and managing machine learning models in production environments. This paper investigates how Kubernetes can be leveraged within MLOps (Machine Learning Operations) and DevOps frameworks, facilitating effective model management, continuous integration, and deployment. We explore the architectural components of Kubernetes that enhance ML workflows, focusing on its orchestration capabilities, support for microservices, and integration with CI/CD pipelines. Additionally, the paper discusses challenges faced by data scientists and DevOps engineers in adopting Kubernetes, alongside best practices for optimizing the deployment of machine learning models. Finally, we present case studies illustrating successful implementations of Kubernetes in MLOps and DevOps, highlighting the transformative impact on productivity and model performance.

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References

  1. Gayam, Swaroop Reddy. "Deep Learning for Autonomous Driving: Techniques for Object Detection, Path Planning, and Safety Assurance in Self-Driving Cars." Journal of AI in Healthcare and Medicine 2.1 (2022): 170-200.
  2. Thota, Shashi, et al. "MLOps: Streamlining Machine Learning Model Deployment in Production." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 186-206.
  3. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Real-Time Logistics and Transportation Optimization in Retail Supply Chains: Techniques, Models, and Applications." Journal of Machine Learning for Healthcare Decision Support 1.1 (2021): 88-126.
  4. Putha, Sudharshan. "AI-Driven Predictive Analytics for Supply Chain Optimization in the Automotive Industry." Journal of Science & Technology 3.1 (2022): 39-80.
  5. Sahu, Mohit Kumar. "Advanced AI Techniques for Optimizing Inventory Management and Demand Forecasting in Retail Supply Chains." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 190-224.
  6. Kasaraneni, Bhavani Prasad. "AI-Driven Solutions for Enhancing Customer Engagement in Auto Insurance: Techniques, Models, and Best Practices." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 344-376.
  7. Kondapaka, Krishna Kanth. "AI-Driven Inventory Optimization in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 377-409.
  8. Kasaraneni, Ramana Kumar. "AI-Enhanced Supply Chain Collaboration Platforms for Retail: Improving Coordination and Reducing Costs." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 410-450.
  9. Pattyam, Sandeep Pushyamitra. "Artificial Intelligence for Healthcare Diagnostics: Techniques for Disease Prediction, Personalized Treatment, and Patient Monitoring." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 309-343.
  10. Kuna, Siva Sarana. "Utilizing Machine Learning for Dynamic Pricing Models in Insurance." Journal of Machine Learning in Pharmaceutical Research 4.1 (2024): 186-232.
  11. Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.
  12. Venkata, Ashok Kumar Pamidi, et al. "Implementing Privacy-Preserving Blockchain Transactions using Zero-Knowledge Proofs." Blockchain Technology and Distributed Systems 3.1 (2023): 21-42.
  13. Reddy, Amit Kumar, et al. "DevSecOps: Integrating Security into the DevOps Pipeline for Cloud-Native Applications." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 89-114.