COPYRIGHT YEAR

2016

AUTHORS

Gabriel Iuhasz, Dana Petcu, Daniel Pop

TITLE

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

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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1 book-chapters:26d4202c0929fa776518cbd7bc11579f sg:abstract 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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23 sg:scigraphId 26d4202c0929fa776518cbd7bc11579f
24 sg:title Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data
25 sg:webpage https://link.springer.com/10.1007/978-3-319-31861-5_7
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27 rdfs:label BookChapter: Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data
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