How is Kubernetes used in data science?

Kubernetes is an open source platform for container orchestration. It enables data scientists to deploy containers as scalable web applications, and provides a variety of configuration options for exposing services on the web.

How Docker is used in data science?

As of late 2020, knowing Docker is almost mandatory for data science jobs. Think of Docker as a virtual machine without an operating system. Docker allows applications to use the same kernel as the system they are running on. As a result, you get both increase in performance and a decrease in the file size.

Is Kubernetes required for data scientist?

Tools that require data scientists to use Kubernetes to train models. This may sound like the same thing but it’s not — making hardware more accessible is great, but not if you force data scientists to understand Kubernetes first. Kubernetes was built by software engineers, for software engineers.

Why Kubernetes has become so popular in data engineering?

Kubernetes has become so universally popular that plenty of tools have been built or redesigned to work on K8s clusters. With Kubernetes, you can leverage the same container orchestration platform to execute ETL jobs, train and serve ML models, and even build visualizations with tools that work on Kubernetes.

What is Kubernetes data?

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. K8s as an abbreviation results from counting the eight letters between the “K” and the “s”. Google open-sourced the Kubernetes project in 2014.

Why is Docker useful?

Developers can create containers without Docker, but the platform makes it easier, simpler, and safer to build, deploy and manage containers. Docker is essentially a toolkit that enables developers to build, deploy, run, update, and stop containers using simple commands and work-saving automation through a single API.

Why do you think this basic knowledge about Docker is important for you to learn?

The key benefit of Docker is that it allows users to package an application with all of its dependencies into a standardized unit for software development. Unlike virtual machines, containers do not have high overhead and hence enable more efficient usage of the underlying system and resources.