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DevOps for Data Science

4 months ago

DevOps is the practice of operations and development engineers collaborating to the whole life cycle of a service, contributing to its various parts, from design through the development process to production support. Although it is not necessarily data science related, it is often the case that a DevOps team exists in companies that perform data science, dedicated (at least in part) to the data products the data scientists architect.

DevOps make use of a number of technologies that facilitate the data science pipeline, particularly when it comes to the deployment stage, whereby the data products get deployed to a server (e.g. on the cloud). Such technologies include Kubernetes and Docker, for containing programming environments, as well as Spark, including Spark streaming. Spark is a more multi-purpose big data platform, while its streaming part is responsible for handling streaming data. Naturally, there are other technologies related to DevOps, depending on the domain of the company, as well as other factors (e.g. what programming stack the company is using for its other projects).

Although oftentimes a data scientist is able to handle all of these programs, whenever there is a DevOps team available, it makes the whole matter better for everyone. After all, a DevOps team comprises of experts in these technologies, while the data scientist has other tasks to focus on that are very difficult to outsource to other tech professionals.

Here at ResourceFlow we have access to several such quality people, who are not only competent in DevOps tech, but also good team players. Feel free to reach out to us to discuss your DevOps and Data Science related needs and work out a spec for your future employees on +44 208 133 0822.


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