An Empirical Study on the Usage of Automated Machine Learning Tools

Majidi, Forough; Openja, Moses; Khomh, Foutse; Li, Heng

doi:10.1109/icsme55016.2022.00014

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…We also aim to evaluate the performance of our methods on the large models that also include non-tabular dataset. This study will help spearhead the standard procedure when automating the development, testing, deployment [54,55], and maintaining fairer machine learning workflow [50].…”

Section: Discussionmentioning

confidence: 99%

Studying the Practices of Deploying Machine Learning Projects on Docker

Openja

Majidi

Khomh

et al. 2022

The International Conference on Evaluation and Assessment in Software Engineering 2022

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Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code. Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is used to deploy ML-based projects. In this study, we conducted an exploratory study to understand how Docker is being used to deploy ML-based projects. As the initial step, we examined the categories of ML-based projects that use Docker. We then examined why and how these projects use Docker, and the characteristics of the resulting Docker images. Our results indicate that six categories of ML-based projects use Docker for deployment, including ML Applications, MLOps/ AIOps, Toolkits, DL Frameworks, Models, and Documentation. We derived the taxonomy of 21 major categories representing the purposes of using Docker, including those specific to models such as model management tasks (e.g., testing, training). We then showed that ML engineers use Docker images mostly to help with the platform portability, such as transferring the software across the operating systems, runtimes such as GPU, and language constraints. However, we also found that more resources may be required to run the Docker images for building ML-based software projects due to the large number of files contained in the image layers with deeply nested directories. We hope to shed light on the emerging practices of deploying ML software projects using containers and highlight aspects that should be improved. CCS Concepts• Machine Learning → Deployment; • >Machine Learning → Docker.

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Section: Discussionmentioning

confidence: 99%

Studying the Practices of Deploying Machine Learning Projects on Docker

Openja

Majidi

Khomh

et al. 2022

The International Conference on Evaluation and Assessment in Software Engineering 2022

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“…I. "An Empirical Study on the Usage of Automated Machine Learning Tools" [9] analyzes the popularity and usage patterns of AutoML tools in GitHub projects, discussing their purposes and common combinations.…”

Section: Background and Related Workmentioning

confidence: 99%

AutoML Platform for Comparative Analysis of Machine Learning Models

Vinod,

Balasaheb,

Pandurang

et al. 2024

Preprint

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Automated Machine Learning (AutoML) platforms have emerged as indispensable tools in facilitating efficient algorithm selection for diverse machine learning tasks. In this study, we introduce a novel AutoML platform designed to empower users with seamless comparative analysis of machine learning algorithms. Our platform offers a user-friendly interface, guiding users through the process of uploading datasets, selecting algorithms, and evaluating performance metrics. Leveraging automation and in-depth analysis, users can effortlessly compare the performance of two selected algorithms, gaining insights into their data-driven projects. Through visualization tools and explainability mechanisms, our platform aids users in making informed decisions for optimal algorithm selection. By addressing the complexities of algorithm selection and enhancing accessibility, our AutoML platform contributes to advancing data-driven decision-making across various domains.

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“…Automated machine learning was developed with the main aim of reducing the human effort required when adjusting the learning settings [60,61]. Thus, AutoML is a technique used to define the parameters and algorithms that will be adopted during learning in order to obtain better results, given that the settings will be made according to the problem being studied [62,63].…”

Section: Automated Machine Learningmentioning

confidence: 99%

“…In this context, due to the improvements observed through the use of AutoML and the growing need for increasingly robust learning systems, in order to cope with the abundance of data that is constantly emerging, this technique has also been used to automate other stages of the learning process [60,63,64].…”

Section: Automated Machine Learningmentioning

confidence: 99%

Transfer Reinforcement Learning for Combinatorial Optimization Problems

Souza,

Santos,

Ottoni

et al. 2024

Algorithms

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Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.

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An Empirical Study on the Usage of Automated Machine Learning Tools

Cited by 15 publications

References 23 publications

Studying the Practices of Deploying Machine Learning Projects on Docker

Studying the Practices of Deploying Machine Learning Projects on Docker

AutoML Platform for Comparative Analysis of Machine Learning Models

Transfer Reinforcement Learning for Combinatorial Optimization Problems

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