Machine Learning-as-a-Service and Its Application to Medical Informatics

  • Ahmad P. Tafti
  • Eric LaRose
  • Jonathan C. Badger
  • Ross Kleiman
  • Peggy Peissig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10358)


Machine learning as an advanced computational technology has been around for several years in discovering patterns from diverse biomedical data sources and providing excellent capabilities ranging from gene annotation to predictive phenotyping. However, machine learning strategies remain underused in small and medium-scale biomedical research labs where they have been collaboratively providing a reasonable amount of scientific knowledge. While most machine learning algorithms are complicated in code, theses labs and individual researchers could accomplish iterative data analysis using different machine learning techniques if they had access to highly available machine learning components and powerful computational infrastructures. In this contribution, we provide a comparison of several state-of-the-art Machine Learning-as-a-Service platforms along with their capabilities in medical informatics. In addition, we performed several analyses to examine the qualitative and quantitative attributes of two Machine Learning-as-a-Service environments namely “BigML” and “Algorithmia”.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Biomedical Informatics Research CenterMarshfield Clinic Research InstituteMarshfieldUSA
  2. 2.Computation and Informatics in Biology and MedicineUniversity of Wisconsin-MadisonMadisonUSA

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