International Journal of Advanced Culture Technology

  • 제6권4호
  • /
  • Pages.303-308
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  • 2018
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  • 2288-7202(pISSN)
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  • 2288-7318(eISSN)
국제문화기술진흥원 (The International Promotion Agency of Culture Technology)
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Analysis on Trends of Machine Learning-as-a-Service

  • Lee, Yo-Seob (School of ICT Convergence, Pyeongtaek University)
  • 투고 : 2018.11.26
  • 심사 : 2018.12.05
  • 발행 : 2018.12.31
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초록

Demand is increasing rapidly in recent years than supply to machine learning professionals. To alleviate this gap, user-friendly machine learning software that can be used by non-specialists has emerged, which is Machine Learning-as-a-Service(MLaaS). MLaaS provides services that enable businesses to easily leverage ML capabilities without expertise. In this paper, we will compare and analyze features, interfaces, supporting programming language, ML framework, and Machine Learning services of MLaaS, to help companies easily use ML service.

키워드

E1GMBY_2018_v6n4_303_f0001.png 이미지

Figure 1. Microsoft Azure ML Studio

E1GMBY_2018_v6n4_303_f0002.png 이미지

Figure 2. IBM Watson Studio

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Figure 3. BigML Dashboard

Table 1. MLaaS’s Features and Interfaces

E1GMBY_2018_v6n4_303_t0001.png 이미지

Table 2. MLaaS’s Programming Languages and ML Frameworks

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Table 3. MLaaS’s Machine Learning Services

E1GMBY_2018_v6n4_303_t0003.png 이미지

참고문헌

  1. Meet MLaaS: Why Machine Learning as as Service is IT's next great enabler, http://techgenix.com/meet-mlaas/.
  2. Top 5 Machine Learning-as-a-Service providers, https://jaxenter.com/top-5-machine-leaarning-service-providers-141275.html.
  3. Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, https://www.kdnuggets.com/2018/01/mlaas-amazon-microsoft-azure-google-cloud-ai.html.
  4. Microsoft Azure Machine Learning Stduio, https://azure.microsoft.com/en-us/services/machine-learning-studio/.
  5. AWS Machine Learning, https://aws.amazon.com/aml/.
  6. Amazon SageMaker, https://aws.amazon.cpm/sagemaker/.
  7. IBM Watson Machine Learning, https://www.ibm.com/cloud/machine-learning.
  8. Watson Studio, https://www.ibm.com/cloud/watson-studio.
  9. Google Cloud Machine Learning Engine, https://cloud.google.com/ml-engine/.
  10. BigML, https://bigml.com.