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How are container technologies like Docker and Kubernetes contributing to the field of data science?

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Actually they are not directly contributing to it, but indirectly? Surely. Kubernetes and Docker in themselves have nothing to do with data science. Rather, they are tools that can be leveraged to ship and deploy software in the cloud. If you have a software running on Kubernetes as microservices, databases and whatnot, it has become easier to integrate data-scientific software with that. Kubernetes leverages Docker’s capabilities and turns Docker containers into entities (pods, deployments, services) that can communicate with each other on a high level.

Kubernetes and Docker can certainly make it easier for data scientists to tap into already existing flows of data without having to set up this by themselves, allowing them to instead focus on research. If you want to learn both these technologies then you must have a look at the following DevOps Training Course. If you are more into reading then you must read the following DevOps Tutorial. Here is the link of the video by which I came to know about it.


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