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

DevOps and MLOps Integration for Data-Driven Decision-Making: Improving Business Agility and Innovation

PDF
  • Michael Thompson
Michael Thompson
PhD, Lead Data Scientist, Innovative Solutions, Boston, USA

Published 04-10-2024

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]
M. Thompson, “DevOps and MLOps Integration for Data-Driven Decision-Making: Improving Business Agility and Innovation”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 99–105, Oct. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/178

Abstract

In today’s rapidly evolving business landscape, organizations are increasingly reliant on data-driven decision-making to maintain a competitive edge. The integration of DevOps and MLOps frameworks presents a significant opportunity to enhance business agility and foster innovation. This paper explores how combining these two methodologies can streamline the deployment of machine learning models and facilitate rapid experimentation. By breaking down silos between development, operations, and data science teams, organizations can improve collaboration, accelerate delivery times, and enhance the overall quality of machine learning outputs. The paper highlights key strategies for integrating DevOps and MLOps, including the implementation of continuous integration/continuous deployment (CI/CD) pipelines, automated monitoring, and feedback loops. Furthermore, the research discusses real-world case studies that demonstrate the effectiveness of this integration in driving business outcomes. Ultimately, the paper argues that embracing a cohesive DevOps and MLOps strategy is essential for organizations seeking to leverage data for informed decision-making and sustained innovation.

PDF

Downloads

Download data is not yet available.

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.

Similar Articles

You may also start an advanced similarity search for this article.