IJBTT

MLOps Antipatterns and Mitigation Approaches
	© 2024 by IJCTT Journal
	Volume-72 Issue-2
	Year of Publication : 2024
	Authors : Ankit Virmani, Manoj Kuppam
	DOI :  10.14445/22312803/IJCTT-V72I2P102

How to Cite?

Ankit Virmani, Manoj Kuppam, "MLOps Antipatterns and Mitigation Approaches," International Journal of Computer Trends and Technology, vol. 72, no. 2, pp. 9-15, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I2P102

Abstract
Deploying machine learning models for various analytics and data applications at an enterprise scale brings diverse challenges. This paper breaks down these challenges and details the critical MLOps antipatterns - the practices to avoid while deploying the machine learning models. Like design patterns formalize software engineering wisdom, antipatterns help us recognize and communicate problematic methodologies. Some of these antipatterns stem from technical errors, while others arise from a lack of understanding of the context of ML usage. This paper aims to facilitate better documentation, collaboration, and faster problem resolution by establishing a common language around these antipatterns. In addition to outlining the antipatterns, the study provides solutions and best practices and suggests a path to a more mature MLOps approach.

Keywords
MLOps, Python, Security, Data, Deployment, DevOps.

Reference

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