2022
DOI: 10.48550/arxiv.2202.10169
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Machine Learning Operations: A Survey on MLOps Tool Support

Abstract: Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn techniques and processes individually using computer algorithms, which is also considered to be a role of Artificial Intelligence (AI). In this paper, we study current technical issues related to software development and delivery in organizations that work on ML projects. The… Show more

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Cited by 11 publications
(10 citation statements)
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“…Operational challenges. In productive settings, it is challenging to operate ML manually due to different stacks of software and hardware components and their interplay as well as the selection of both ( [64], [65]. Therefore, robust automation is required [24], [33].…”
Section: Open Challengesmentioning
confidence: 99%
“…Operational challenges. In productive settings, it is challenging to operate ML manually due to different stacks of software and hardware components and their interplay as well as the selection of both ( [64], [65]. Therefore, robust automation is required [24], [33].…”
Section: Open Challengesmentioning
confidence: 99%
“…There are various practical fields where researchers and companies have applied CL. Publications in computer vision [13], Machine Learning Model Operationalization Management (MLOps) [85], robotics and machine vision, industry, and edge computing, have expanded knowledge from the theoretical to more practical situations. An overview of some of the latest and most relevant CL studies for different applications is presented in the following sections:…”
Section: Real-world Applications Of Continual Learningmentioning
confidence: 99%
“…Среди крупных недостатков MLFlow выделяется проблема отсутствия ролей пользователей и функционала безопасности, что приводит к затруднениям совместной работы над моделями [21]. Кроме того, еще одни из проблем MLFlowсложности с развёртыванием моделей на различных платформах и отсутствие мониторинга производительности моделей [21], [22].…”
Section: обзор существующих платформunclassified
“…К недостаткам Kubeflow относится отсутствие версионирования данных и конвейера [22]. Кроме этого, Kubeflow сложно настроить, у него высокий порог входа, а для его использования необходимы глубокие знания в Kubernetes [26].…”
Section: обзор существующих платформunclassified