Challenges and Experiences of Iranian Developers with MLOps at
Enterprise
- URL: http://arxiv.org/abs/2402.12281v1
- Date: Mon, 19 Feb 2024 16:48:42 GMT
- Title: Challenges and Experiences of Iranian Developers with MLOps at
Enterprise
- Authors: Mohammad Heydari, Zahra Rezvani
- Abstract summary: This research explores the challenges and experiences of Iranian developers in implementing the MLOps paradigm within enterprise settings.
We review the most popular MLOps tools used by leading technology enterprises.
The findings reveal that data quality problems, a lack of resources, and difficulties in model deployment are among the primary challenges faced by practitioners.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data is becoming more complex, and so are the approaches designed to process
it. Enterprises have access to more data than ever, but many still struggle to
glean the full potential of insights from what they have. This research
explores the challenges and experiences of Iranian developers in implementing
the MLOps paradigm within enterprise settings. MLOps, or Machine Learning
Operations, is a discipline focused on automating the continuous delivery of
machine learning models. In this study, we review the most popular MLOps tools
used by leading technology enterprises. Additionally, we present the results of
a questionnaire answered by over 110 Iranian Machine Learning experts and
Software Developers, shedding light on MLOps tools and the primary obstacles
faced. The findings reveal that data quality problems, a lack of resources, and
difficulties in model deployment are among the primary challenges faced by
practitioners. Collaboration between ML, DevOps, Ops, and Science teams is seen
as a pivotal challenge in implementing MLOps effectively.
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