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Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data

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Data Science and Big Data Computing

Abstract

Applying popular machine learning algorithms to large amounts of data has raised new challenges for machine learning practitioners. Traditional libraries do not support properly the processing of huge data sets, so the new approaches are needed. Using modern distributed computing paradigms, such as MapReduce or in-memory processing, novel machine learning libraries have been developed. At the same time, the advance of cloud computing in the past 10 years could not be ignored by the machine learning community. Thus, a rise of cloud-based platforms has been of significance. This chapter aims at presenting an overview of novel platforms, libraries, and cloud services that can be used by data scientists to extract knowledge from unstructured and semi-structured, large data sets. The overview covers several popular packages to enable distributed computing in popular machine learning environments, distributed platforms for machine learning, and cloud services for machine learning, known as machine-learning-as-a-service approach. We also provide a number of recommendations for data scientists when considering machine learning approach for their problem.

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Notes

  1. 1.

    http://cloudnumbers.com

  2. 2.

    http://cs.croakun.com

  3. 3.

    http://opani.com

  4. 4.

    www.revolutionanalytics.com

  5. 5.

    http://datasciencetoolbox.org

  6. 6.

    http://cran.r-project.org/web/packages/available_packages_by_name.html

  7. 7.

    http://www.stat.purdue.edu/~sguha/rhipe/doc/html/index.html

  8. 8.

    http://code.google.com/p/segue

  9. 9.

    http://aws.amazon.com/elasticmapreduce

  10. 10.

    https://github.com/crs4/pydoop

  11. 11.

    https://store.continuum.io/cshop/anaconda

  12. 12.

    http://ipython.org/ipython-doc/dev/parallel/

  13. 13.

    http://star.mit.edu/cluster/

  14. 14.

    https://github.com/shadanan/HadoopLink

  15. 15.

    http://www.mathworks.com/help/matlab/large-files-and-big-data.html

  16. 16.

    http://www.mathworks.com/help/distcomp/big-data.html

  17. 17.

    https://www.knime.org/knime-big-data-extension

  18. 18.

    https://rapidminer.com/products/radoop/

  19. 19.

    http://petuum.org

  20. 20.

    http://jubat.us

  21. 21.

    https://dato.com/products/create

  22. 22.

    https://flink.apache.org/

  23. 23.

    http://0xdata.com/product/

  24. 24.

    https://github.com/JohnLangford/vowpal_wabbit/

  25. 25.

    http://julialang.org/

  26. 26.

    http://mlbasejl.readthedocs.org/en/latest/

  27. 27.

    http://oryxproject.github.io/oryx/

  28. 28.

    https://github.com/cloudera/oryx

  29. 29.

    http://research.microsoft.com/en-us/projects/dryad/

  30. 30.

    http://msdn.microsoft.com/netframework/future/linq/

  31. 31.

    http://deeplearning4j.org

  32. 32.

    https://samoa.incubator.apache.org/

  33. 33.

    http://www.dmg.org/v4-1/GeneralStructure.html

  34. 34.

    https://prediction.io/

  35. 35.

    http://www.ersatzlabs.com/

  36. 36.

    http://www.skymind.io/about/

  37. 37.

    http://docs.aws.amazon.com/machine-learning/latest/mlconcepts/mlconcepts.html

  38. 38.

    https://developers.google.com/prediction/

  39. 39.

    https://developers.google.com/storage/

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Acknowledgments

This work was supported by the European Commission H2020 co-funded with project DICE (GA 644869).

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Authors and Affiliations

  1. Institute e-Austria Timisoara, West University of Timisoara, Blvd. Vasile Parvan, nr. 4, 300223, Timișoara, Romania

    Daniel Pop, Gabriel Iuhasz & Dana Petcu

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  1. Daniel Pop
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Correspondence to Daniel Pop .

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  1. Department of Computing and Mathematics , University of Derby, Derby, United Kingdom

    Zaigham Mahmood

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Pop, D., Iuhasz, G., Petcu, D. (2016). Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data. In: Mahmood, Z. (eds) Data Science and Big Data Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-31861-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-31861-5_7

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