DevOps and MLOps Convergence
Improving Collaboration Between Data Science and Engineering Teams
Keywords:
DevOps, MLOps, data science, software engineering, collaborationAbstract
The rapid advancement of artificial intelligence (AI) and machine learning (ML) has necessitated a paradigm shift in how organizations approach software development and deployment. The convergence of DevOps and MLOps represents a critical evolution in this landscape, focusing on enhancing collaboration between data science and engineering teams. This paper addresses the core principles of DevOps and MLOps, their complementary roles, and the practical implications of their integration. By fostering improved communication, aligning tools, and creating a culture of shared accountability, organizations can streamline machine learning projects, reduce time to market, and improve overall project success. This paper concludes by discussing the challenges of implementing this convergence and proposing strategies for overcoming these barriers, ultimately highlighting the transformative potential of DevOps and MLOps collaboration in the modern data-driven landscape.
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