Challenges in Deploying Machine Learning: A Survey of Case Studies

被引:143
|
作者
Paleyes, Andrei [1 ]
Urma, Raoul-Gabriel [2 ]
Lawrence, Neil D. [1 ]
机构
[1] Univ Cambridge, Dept Comp Sci & Technol, Cambridge, England
[2] Cambridge Spark, Cambridge, England
基金
英国科研创新办公室;
关键词
Machine learning applications; sofware deployment; DEPLOYMENT; CLASSIFICATION; TECHNOLOGIES;
D O I
10.1145/3533378
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries, and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. By mapping found challenges to the steps of the machine learning deployment workflow, we show that practitioners face issues at each stage of the deployment process. The goal of this article is to lay out a research agenda to explore approaches addressing these challenges.
引用
收藏
页数:29
相关论文
共 50 条
  • [41] A survey of 5G network systems: challenges and machine learning approaches
    Fourati, Hasna
    Maaloul, Rihab
    Chaari, Lamia
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (02) : 385 - 431
  • [42] Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges
    Qurratulain Khan, M.
    Gaber, Abdo
    Schulz, Philipp
    Fettweis, Gerhard
    IEEE ACCESS, 2023, 11 : 11880 - 11902
  • [43] Challenges, Methods, Data-A Survey of Machine Learning in Water Distribution Networks
    Vaquet, Valerie
    Hinder, Fabian
    Artelt, Andre
    Ashraf, Inaam
    Strotherm, Janine
    Vaquet, Jonas
    Brinkrolf, Johannes
    Hammer, Barbara
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING-ICANN 2024, PT IX, 2024, 15024 : 155 - 170
  • [44] A survey of machine learning and evolutionary computation for antenna modeling and optimization: Methods and challenges
    Zou, Hanhua
    Zeng, Sanyou
    Li, Changhe
    Ji, Jingyu
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 138
  • [45] A survey of 5G network systems: challenges and machine learning approaches
    Hasna Fourati
    Rihab Maaloul
    Lamia Chaari
    International Journal of Machine Learning and Cybernetics, 2021, 12 : 385 - 431
  • [46] Challenges for machine learning in clinical translation of big data imaging studies
    Dinsdale, Nicola K.
    Bluemke, Emma
    Sundaresan, Vaanathi
    Jenkinson, Mark
    Smith, Stephen M.
    Namburete, Ana I. L.
    NEURON, 2022, 110 (23) : 3866 - 3881
  • [47] Moroccan's Arabic Speech Training And Deploying Machine Learning Models with Teachable Machine
    Jebbar, Mostafa
    Maizate, Abderrahim
    Ait Abdelouahid, Rachida
    Procedia Computer Science, 2022, 203 : 801 - 806
  • [48] Machine-to-Machine Communication and Research Challenges: A Survey
    Zhao, Ming
    Kumar, Arun
    Ristaniemi, Tapani
    Chong, Peter Han Joo
    WIRELESS PERSONAL COMMUNICATIONS, 2017, 97 (03) : 3569 - 3585
  • [49] Machine-to-Machine Communication and Research Challenges: A Survey
    Ming Zhao
    Arun Kumar
    Tapani Ristaniemi
    Peter Han Joo Chong
    Wireless Personal Communications, 2017, 97 : 3569 - 3585
  • [50] Challenges in translational machine learning
    Artuur Couckuyt
    Ruth Seurinck
    Annelies Emmaneel
    Katrien Quintelier
    David Novak
    Sofie Van Gassen
    Yvan Saeys
    Human Genetics, 2022, 141 : 1451 - 1466