5 Reasons Data Scientists Should Adopt DevOps Practices - InformationWeek
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Lisa Morgan
Lisa Morgan
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5 Reasons Data Scientists Should Adopt DevOps Practices

Enterprise software development teams have historically had trouble ensuring the code that runs well on a developer's machine also runs well in production. DevOps has promoted more collaboration between developers and IT operations. Data scientists and data science teams face similar challenges, which DevOps concepts can help address.
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As the pace of business continues to accelerate, software and data science teams find themselves under pressure to deliver more business value in less time. Software publishers and enterprise development teams have attempted to address the issue with Agile development practices which are cross-functional in nature, although Agile practices do not guarantee that the code running on a developer's machine will work the same way in production. DevOps closes the gap by promoting collaboration between development and IT operations and enabling project visibility across development and IT operations, which accelerates the delivery of better quality software.

Data scientists and data science teams often face challenges that are similar to the challenges software development teams face. For example, some of them lack the cross-functional collaboration and support they need to ensure their work is timely and actually provides business value. In addition, their algorithms and models don't always operate as they should in production because conditions or the data have changed.

[Data science and DevOps share the same venue when Interop ITX 2018 opens on April 30 in Las Vegas. Two main tracks for session presentations are DevOps and Data&Analytics.]

"For all the work data scientists put into designing, testing and optimizing their algorithms, the real tests come when they are put into use," said Michael Fauscette, chief research officer at business solutions review platform provider G2 Crowd. "From Facebook's newsfeed to stock market 'flash crashes,' we see what happens when algorithms go bad. The best algorithms must be continuously tested and improved."

DevOps practices can help data scientists address some of the challenges they face, but it's not a silver bullet. Data science has some notable differences that also need to be considered.

Following are a few things data scientists and their organizations should consider.

Image: Pixabay
Image: Pixabay



Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio

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