Applying devops in data science and machine learning | Machine Learning - InfoWorld

Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key, summarizes Isaac Sacolick, Contributing Editor.

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Data scientists have some practices and needs in common with software developers. Both data scientists and software engineers plan, architect, code, iterate, test, and deploy code to achieve their goals. For software developers this often means custom coding applications and microservices; data scientists implement data integrations with dataops, make predictions through analytical models, and create dashboards to help end users navigate results.

Devops engineers looking to automate and collaborate with operational engineers should expand their scope and also provide services to data scientists as part of their charter...

Start with the data scientist experience
Like application developers, data scientists are most interested in solving problems, are very involved in configuring their tools, and often have less interest in configuring infrastructure. But unlike software developers, data scientists may not have the same experience and background to fully configure their development workflows. This presents an opportunity for devops engineers to treat data scientists as customers, help define their requirements, and take ownership in delivering solutions.
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Source: InfoWorld