Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Achieving End-to-End Automation: Combining DevOps and MLOps for Streamlined Data and Model Workflows

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  • Alexandra Thompson
Alexandra Thompson
PhD, Senior Research Scientist, Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Published 18-09-2024

Keywords

  • DevOps,
  • MLOps
Creative Commons License

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

How to Cite

[1]
A. Thompson, “Achieving End-to-End Automation: Combining DevOps and MLOps for Streamlined Data and Model Workflows”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 62–68, Sep. 2024, Accessed: Dec. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/173

Abstract

The growing complexity of machine learning (ML) workflows and data engineering has necessitated the integration of DevOps practices with MLOps to achieve end-to-end automation. This paper explores the convergence of these two methodologies, highlighting how they can streamline data and model workflows for enhanced efficiency, collaboration, and continuous delivery. By analyzing the key components of DevOps and MLOps, this study identifies best practices and tools that facilitate seamless integration, ultimately fostering a culture of collaboration between data scientists and IT operations teams. Furthermore, the paper discusses the benefits of end-to-end automation, including improved model performance, faster deployment cycles, and enhanced reproducibility. The findings underscore the importance of adopting a holistic approach to automation in data and ML workflows, paving the way for organizations to leverage the full potential of their data assets.

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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.
  14. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
  15. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  16. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010.
  17. C. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
  18. D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
  19. Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.
  20. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097–1105.

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