Since it was launched, Kubernetes technology has transformed the way software developers build and deploy applications. The widespread adoption of this technology intrigued data scientists, who saw that Kubernetes also provides features to optimize and support the data science workflow.
In this post, I’ll explain what is Kubernetes and how it can help support data science activities. I’ll also provide some examples of use cases to demonstrate the various benefits that made data scientists fall in love with Kuberenetes technology.
What Is Kubernetes?
Kubernetes is an open-source platform designed to manage containers and clusters in a single interface. You can deploy containers to clusters across all types of environments, including clouds, virtual machines and physical machines, creating a network of mini virtual machines. In Kubernetes, one or more containers are placed in a pod, which is the smallest possible unit that can be deployed.
The platform lets you scale applications according to your workload. It is extensible, in that it allows application components to move across systems.
Kubernetes main features include:
Automating manual processes—manages container hosting and deployment.
Self-monitoring—checks the health of containers and nodes
Horizontal scaling—you can scale out the container applications to accommodate fluctuating workloads.
Flexibility—runs everywhere, on-premises, hybrid or cloud infrastructure, allowing you to move workloads …