End-to-End MLOps for Scalable Model Deployment: Engineering Best Practices for Efficient and Reliable Machine Learning Systems

End-to-End MLOps for Scalable Model Deployment: Engineering Best Practices for Efficient and Reliable Machine Learning Systems

	© 2024 by IJCTT Journal
	Volume-72 Issue-11
	Year of Publication : 2024
	Authors : Koushik Balaji Venkatesan
	DOI : 10.14445/22312803/IJCTT-V72I11P118

How to Cite?

Koushik Balaji Venkatesan , "End-to-End MLOps for Scalable Model Deployment: Engineering Best Practices for Efficient and Reliable Machine Learning Systems," International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 165-171, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I11P118

Abstract
Machine Learning Operations (MLOps) help integrate machine learning model development with production deployment using best practices from software engineering. The machine learning life cycle brings unique problems, and this paper outlines possible approaches to address and fix them. Key MLOps practices are reviewed, focusing on Continuous Integration and Continuous Deployment (CI/CD), automated testing, and adaptive scaling strategies. Techniques for deploying models based on latency and traffic demands are explored, including traffic routing and shadow deployments. Advanced strategies such as canary releases, A/B testing, automated monitoring and retraining are also discussed. The goal is for organizations to increase reliability, reduce downtime, create scalable, robust ML pipelines, and accelerate innovation by incorporating engineering best practices.

Keywords
CI/CD, Load balancing, Machine learning, MLOps, Shadow testing.

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IJBTT

End-to-End MLOps for Scalable Model Deployment: Engineering Best Practices for Efficient and Reliable Machine Learning Systems