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Analysis on Trends of Machine Learning-as-a-Service

  • Lee, Yo-Seob (School of ICT Convergence, Pyeongtaek University)
  • Received : 2018.11.26
  • Accepted : 2018.12.05
  • Published : 2018.12.31

Abstract

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.

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Figure 1. Microsoft Azure ML Studio

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Figure 2. IBM Watson Studio

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Figure 3. BigML Dashboard

Table 1. MLaaS’s Features and Interfaces

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Table 2. MLaaS’s Programming Languages and ML Frameworks

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Table 3. MLaaS’s Machine Learning Services

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References

  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.