Automating MLOps Pipelines with GitHub Actions and Azure: A Case Study on Diabetes Prediction

Συγγραφείς: Chikkala, Lokesh Kumar, Pathi, Nishanth Kumar, Abhi, Shinu

Πηγή: 2025 International Conference on Emerging Technologies in Computing and Communication (ETCC) Emerging Technologies in Computing and Communication (ETCC), 2025 International Conference on. :1-6 Jun, 2025

Relation: 2025 International Conference on Emerging Technologies in Computing and Communication (ETCC)

Couler: Unified Machine Learning Workflow Optimization in Cloud

Συγγραφείς: Wang, Xiaoda, Tang, Yuan, Guo, Tengda, Sang, Bo, Wu, Jiewei, Sha, Jian, Zhang, Ke, Qian, Jiang, Tang, Mingjie

Πηγή: 2024 IEEE 40th International Conference on Data Engineering (ICDE) ICDE Data Engineering (ICDE), 2024 IEEE 40th International Conference on. :5224-5237 May, 2024

Relation: 2024 IEEE 40th International Conference on Data Engineering (ICDE)

Machine Learning Workflow Recommendations Based on Hierarchical Topics

Συγγραφείς: Song, Xiaoping, Rao, Wenbi, Liu, Xing

Πηγή: 2024 4th International Conference on Computer, Control and Robotics (ICCCR) Computer, Control and Robotics (ICCCR), 2024 4th International Conference on. :338-342 Apr, 2024

Relation: 2024 4th International Conference on Computer, Control and Robotics (ICCCR)

Developing tunable machine learning workflow for traffic analysis in SDN

Συγγραφείς: Sama Samaan, Hassan Jeiad

Πηγή: Serbian Journal of Electrical Engineering, Vol 22, Iss 2, Pp 183-199 (2025)

Θεματικοί όροι: sdn, machine learning pipeline, spark, traffic analysis, machine learning workflow, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Σύνδεσμος πρόσβασης: https://doaj.org/article/ff2f6f31377e45b59e56228109388064

AI-Driven Scheduling in Print Production: Optimizing Print Workflows with Machine Learning

Συγγραφείς: Sharma, Amit

Θεματικοί όροι: AI scheduling, print production, machine learning, workflow optimization, reinforcement learning, smart manufacturing, predictive analytics, job sequencing, print MIS, ERP integration, make-ready time, print automation, real-time optimization, bottleneck reduction, sustainable printing Ask ChatGPT

Scanflow-K8s: Agent-based Framework for Autonomic Management and Supervision of ML Workflows in Kubernetes Clusters

Συγγραφείς: Liu, Peini, Bravo-Rocca, Gusseppe, Guitart, Jordi, Dholakia, Ajay, Ellison, David, Hodak, Miroslav

Πηγή: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) CCGRID Cluster, Cloud and Internet Computing (CCGrid), 2022 22nd IEEE International Symposium on. :376-385 May, 2022

Relation: 2022 22nd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

An Empirical Study of Testing Machine Learning in the Wild

Συγγραφείς: Openja, Moses, Khomh, Foutse, Foundjem, Armstrong, Jiang, Zhen Ming (Jack), Abidi, Mouna, Hassan, Ahmed E.

Πηγή: ACM Transactions on Software Engineering and Methodology. 34(1):1-63

Διαθεσιμότητα: http://dl.acm.org/doi/10.1145/3680463

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Συγγραφείς: Styliani I. Kampezidou, Archana Tikayat Ray, Anirudh Prabhakara Bhat, Olivia J. Pinon Fischer, Dimitri N. Mavris

Πηγή: Eng, Vol 5, Iss 1, Pp 384-416 (2024)

Θεματικοί όροι: data engineering, extraction, loading, transformation, machine learning workflow, 0202 electrical engineering, electronic engineering, information engineering, numerical data, categorical data, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, supervised learning, TK1-9971

Σύνδεσμος πρόσβασης: https://doaj.org/article/3a4d12e11bf54d67a46bbd9e48785f32

Introducing Data-Driven Materials Informatics into Undergraduate Courses through a Polymer Science Workshop

Συγγραφείς: Mona Amrihesari, Blair Brettmann

Θεματικοί όροι: Biochemistry, Science Policy, Plant Biology, Space Science, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, postworkshop student surveys, machine learning workflow, driven problem solving, driven materials informatics, students ’ understanding, materials course introduces, including data cleaning, polymer science workshop, polymer science, materials discovery, engineering course, course material, undergraduate students, undergraduate education, undergraduate courses, successful adoption, result interpretation, rapid growth, performance evaluation, new concepts, model training, measurable improvement, key steps

Διαθεσιμότητα: https://doi.org/10.1021/acs.jchemed.5c00562.s004
https://figshare.com/articles/journal_contribution/Introducing_Data-Driven_Materials_Informatics_into_Undergraduate_Courses_through_a_Polymer_Science_Workshop/29922827

ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data

Συγγραφείς: Alves, J.M., Honorio, L.M., Capretz, M.A.M.

Πηγή: IEEE Access Access, IEEE. 7:152953-152967 2019

A comprehensive review of cyberbullying-related content classification in online social media

Συγγραφείς: Teng, Teoh Hwai ^a, Varathan, Kasturi Dewi ^{a, ⁎}, Crestani, Fabio ^b

Πηγή: In Expert Systems With Applications 15 June 2024 244

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Συγγραφείς: Styliani I. Kampezidou, Archana Tikayat Ray, Anirudh Prabhakara Bhat, Olivia J. Pinon Fischer, Dimitri N. Mavris

Πηγή: Eng, Vol 5, Iss 1, Pp 384-416 (2024)

Θεματικοί όροι: machine learning workflow, supervised learning, numerical data, categorical data, data engineering, extraction, loading, transformation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Περιγραφή αρχείου: electronic resource

Relation: https://www.mdpi.com/2673-4117/5/1/21; https://doaj.org/toc/2673-4117

Σύνδεσμος πρόσβασης: https://doaj.org/article/3a4d12e11bf54d67a46bbd9e48785f32

Automating Extract, Transform, Load (ETL) Pipelines using Machine Learning Triggered Workflow Optimization

Συγγραφείς: Samyukta Rongala

Πηγή: International Journal of Intelligent Systems and Applications in Engineering; Vol. 12 No. 3 (2024); 4427–4434

Θεματικοί όροι: Data Integration, Data Engineering Solutions, Data Processing, Extract, Transform, Load (ETL) Pipeline Automation, Machine Learning, Workflow Optimization

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://www.ijisae.org/index.php/IJISAE/article/view/7193

GradeML: Towards Holistic Performance Analysis for Machine Learning Workflows: Towards holistic performance analysis for machine learning workflows

Συγγραφείς: Matthijs F. Jansen, Animesh Trivedi, Tim Hegeman, Alexandru Iosup

Πηγή: Companion of the ACM/SPEC International Conference on Performance Engineering. :57-63

Θεματικοί όροι: MLDevOps, Performance analysis, GradeML, Modeling, 0202 electrical engineering, electronic engineering, information engineering, Data gathering, 02 engineering and technology, SDG 12 - Responsible Consumption and Production, Machine learning workflow

Σύνδεσμος πρόσβασης: https://www.narcis.nl/publication/RecordID/oai%3Aresearch.vu.nl%3Apublications%2Fe2a2a21e-f458-4f86-b6bd-2ca530784ac5
https://dl.acm.org/doi/pdf/10.1145/3447545.3451185
https://research.vu.nl/en/publications/grademl-towards-holistic-performance-analysis-for-machine-learnin

Detailed Implementation of a Reproducible Machine Learning-Enabled Workflow

Συγγραφείς: Schackart III, Kenneth E., Imker, Heidi J., Cook, Charles E.

Πηγή: Data Science Journal, Vol 23, Pp 23-23 (2024)
Data Science Journal; Vol. 23 (2024); 23

Θεματικοί όροι: 0301 basic medicine, biodata resource inventory, Q1-390, 0303 health sciences, 03 medical and health sciences, FAIR data, Science (General), computational reproducibility, open science, machine learning workflow, research software, fair data, Python

Περιγραφή αρχείου: application/pdf; text/xml

Σύνδεσμος πρόσβασης: https://doaj.org/article/8e5d6ede7aaa432a9e65e14932a1a040
https://account.datascience.codata.org/index.php/up-j-dsj/article/view/1668

Process of Medical Dataset Construction for Machine Learning - Multifield Study and Guidelines

Συγγραφείς: Cychnerski, Jan^Aff13, Dziubich, Tomasz^Aff13

Συνεισφορές: Filipe, Joaquim, Editorial Board Member^Aff1, Ghosh, Ashish, Editorial Board Member^Aff2, Prates, Raquel Oliveira, Editorial Board Member^Aff3, Zhou, Lizhu, Editorial Board Member^Aff4, Bellatreche, Ladjel, editor^Aff5, Dumas, Marlon, editor^Aff6, Karras, Panagiotis, editor^Aff7, Matulevičius, Raimundas, editor^Aff8, Awad, Ahmed, editor^Aff9, Weidlich, Matthias, editor^Aff10, Ivanović, Mirjana, editor^Aff11, Hartig, Olaf, editor^Aff12

Πηγή: New Trends in Database and Information Systems : ADBIS 2021 Short Papers, Doctoral Consortium and Workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021, Proceedings. 1450:217-229

Complaint-driven Training Data Debugging for Query 2.0

Συγγραφείς: Wu, Weiyuan, Flokas, Lampros, Wu, Eugene, Wang, Jiannan

Πηγή: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. :1317-1334

Διαθεσιμότητα: http://dl.acm.org/doi/10.1145/3318464.3389696

Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications

Συγγραφείς: Bachinger, Florian^Aff11, Kronberger, Gabriel^Aff11

Συνεισφορές: Goos, Gerhard, Founding Editor^Aff1, Hartmanis, Juris, Founding Editor^Aff2, Bertino, Elisa, Editorial Board Member^Aff3, Gao, Wen, Editorial Board Member^Aff4, Steffen, Bernhard, Editorial Board Member^Aff5, Woeginger, Gerhard, Editorial Board Member^Aff6, Yung, Moti, Editorial Board Member^Aff7, Moreno-Díaz, Roberto, editor^Aff8, Pichler, Franz, editor^Aff9, Quesada-Arencibia, Alexis, editor^Aff10

Πηγή: Computer Aided Systems Theory – EUROCAST 2019 : 17th International Conference, Las Palmas de Gran Canaria, Spain, February 17–22, 2019, Revised Selected Papers, Part I. 12013:263-270

Assuring the Machine Learning Lifecycle : Desiderata, Methods, and Challenges

Συγγραφείς: Ashmore, Rob, Calinescu, Radu, Paterson, Colin

Πηγή: ACM Computing Surveys (CSUR). 54(5):1-39

Διαθεσιμότητα: http://dl.acm.org/doi/10.1145/3453444

Machine Learning

Συγγραφείς: Akerkar, Rajendra^Aff2

Συνεισφορές: Akerkar, Rajendra^Aff1

Πηγή: Artificial Intelligence for Business. :19-32