Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps

• URL: http://arxiv.org/abs/2408.11112v1
• Date: Tue, 20 Aug 2024 18:11:17 GMT
• Title: Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps
• Authors: Diego Nogare, Ismar Frango Silveira,
• Abstract summary: The MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models. This paper contributes to the understanding of MLOps techniques and their most diverse applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Data Science has become increasingly relevant as a support tool for industry, significantly enhancing decision-making in a way never seen before. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. Research results shows MLOps is a constantly evolving discipline, with challenges and solutions for integrating development and production environments, publishing models in production environments, and monitoring models throughout the end to end development lifecycle. This paper contributes to the understanding of MLOps techniques and their most diverse applications.

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