MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases

Granlund, Tuomas; Kopponen, Aleksi; Stirbu, Vlad; Myllyaho, Lalli; Mikkonen, Tommi

doi:10.1109/wain52551.2021.00019

Cited by 30 publications

(13 citation statements)

References 10 publications

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“…Therefore, CI/CD can be seen as a DevOps tactic. [6], [7], [22], [23], [α, β, θ]. P2 Workflow orchestration.…”

Section: A Principlesmentioning

confidence: 99%

“…It takes care of the build, test, delivery, and deploy steps. It provides rapid feedback to developers regarding the success or failure of certain steps, thus increasing the overall productivity [6], [7], [22]- [24], [28], [α, β, γ, ε, ζ, η]. Examples are Jenkins [7], [24], and GitHub actions (η).…”

Section: C1 Ci/cd Component (P1 P6 P9mentioning

confidence: 99%

“…R6 DevOps Engineer. The DevOps engineer bridges the gap between development and operations and ensures proper CI/CD automation, ML workflow orchestration, model deployment to production, and monitoring [7], [22], [25], [51] , [α, β, γ, ε, ζ, η, θ]. R7 ML Engineer/MLOps Engineer.…”

Section: Rolesmentioning

confidence: 99%

“…CI/CD puts ideas of DevOps into practice. Therefore, CI/CD can be seen as a DevOps tactic [6],[7],[22],[23],. [α, β, θ].…”

mentioning

confidence: 99%

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Machine Learning Operations (MLOps): Overview, Definition, and Architecture

2023

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The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue. MLOps includes several aspects, such as best practices, sets of concepts, and development culture. However, MLOps is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we conduct mixed-method research, including a literature review, a tool review, and expert interviews. As a result of these investigations, we contribute to the body of knowledge by providing an aggregated overview of the necessary principles, components, and roles, as well as the associated architecture and workflows. Furthermore, we provide a comprehensive definition of MLOps and highlight open challenges in the field. Finally, this work provides guidance for ML researchers and practitioners who want to automate and operate their ML products with a designated set of technologies.

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“…Therefore, CI/CD can be seen as a DevOps tactic. [6], [7], [22], [23], [α, β, θ]. P2 Workflow orchestration.…”

Section: A Principlesmentioning

confidence: 99%

Section: C1 Ci/cd Component (P1 P6 P9mentioning

confidence: 99%

Section: Rolesmentioning

confidence: 99%

“…CI/CD puts ideas of DevOps into practice. Therefore, CI/CD can be seen as a DevOps tactic [6],[7],[22],[23],. [α, β, θ].…”

mentioning

confidence: 99%

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Machine Learning Operations (MLOps): Overview, Definition, and Architecture

2023

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“…A thematic area close to AutoML concerns the ML Operations (MLOps) prac MLOps constitute a set of tools and mechanisms able to enhance the collaboratio tween data scientists and IT professionals in applying and maintaining ML models [2 In turn, MLOps practices are in the position to assist the risk management associated deploying ML models in production by providing traceability, monitoring, and te capabilities [28,30,31]. In general, two major issues are considered in designing M procedures [28,30,32]. The first one is related to compatibility problems arising from fact that MLOps practices integrate a wide range of heterogeneous tools, technolo and software environments.…”

Section: Introductionmentioning

confidence: 99%

Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline

et al. 2023

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In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks.

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