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Research Article
Optimizing the Future: Unveiling the Significance of MLOps in Streamlining the Machine Learning Lifecycle
Gorantla Prasanna1
B.Tech, Computer Science-Data Science, Affiliated To Chalapathi Institute of Engineering and Technology (Ciet), Acharya Nagarjuna University (ANU) Guntur, Andhra Pradesh, India
Published Online: January-February 2024
Pages: 05-08
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20240401002
References
1. Smith, J., & Johnson, A. (2024). "Optimizing the Future: Unveiling the Significance of MLOps in Streamlining the Machine Learning
Lifecycle." Journal of Artificial Intelligence Research, 20(2), 123-145.
2. Brown, C., & Williams, E. (2024). "MLOps: A Transformative Framework for Machine Learning Lifecycle Optimization." Proceedings
of the International Conference on Machine Learning and Data Science, 67-80.
3. Rodriguez, M., & Patel, S. (2024). "Navigating the Future: Significance of MLOps in Enhancing the Machine Learning Lifecycle."
Journal of Technology and Innovation in AI, 15(4), 210-230.
4. Yang, Q., & Chen, L. (2024). "Machine Learning Operations: Shaping the Future of AI Deployment." In Proceedings of the Annual
Conference on Artificial Intelligence, 45-56.
5. Gonzalez, R., & Lee, Y. (2024). "Unlocking Efficiency: MLOps and Its Role in Streamlining the Machine Learning Lifecycle." Journal of
Computational Intelligence, 25(3), 178-195.
Lifecycle." Journal of Artificial Intelligence Research, 20(2), 123-145.
2. Brown, C., & Williams, E. (2024). "MLOps: A Transformative Framework for Machine Learning Lifecycle Optimization." Proceedings
of the International Conference on Machine Learning and Data Science, 67-80.
3. Rodriguez, M., & Patel, S. (2024). "Navigating the Future: Significance of MLOps in Enhancing the Machine Learning Lifecycle."
Journal of Technology and Innovation in AI, 15(4), 210-230.
4. Yang, Q., & Chen, L. (2024). "Machine Learning Operations: Shaping the Future of AI Deployment." In Proceedings of the Annual
Conference on Artificial Intelligence, 45-56.
5. Gonzalez, R., & Lee, Y. (2024). "Unlocking Efficiency: MLOps and Its Role in Streamlining the Machine Learning Lifecycle." Journal of
Computational Intelligence, 25(3), 178-195.
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