Today we are going to be talking about Deploying a Dash App on Kubernetes with a Helm Chart using the AWS Managed Kubernetes Service EKS.
For this post, I'm going to assume that you have an EKS cluster up and running because I want to focus more on the strategy behind a real-time data visualization platform. If you don't, please check out my detailed project template for building AWS EKS Clusters.
Dash is a data visualization platform written in Python.
Dash is the most downloaded, trusted framework for building ML & data science web apps.
Dash empowers teams to build data science and ML apps that put the power of Python, R, and Julia in the hands of business users. Full stack apps that would typically require a front-end, backend, and dev ops team can now be built and deployed in hours by data scientists with Dash. https://plotly.com/dash/
If you'd like to know what the Dash people say about Dash on Kubernetes you can read all about that here.
Pretty much though, Dash is...
If you're following along with the deploy RShiny on AWS Series, you'll know that I covered deploying RShiny with a helm chart . Today, I want to go deeper into deploying RShiny on EKS, along with some tips and tricks that I use for my everyday deployments.
If you'd like to learn more about deploying RShiny please consider checking out my FREE Deploy RShiny on AWS Guide!
Kubernetes is kind of a beast to get started with, and people constantly complain that its extremely complicated to get started with. They would be correct, but I'm here to give the 2 minute rundown of what you need to know to deploy your RShiny (or Dash, Flask, Django, Ruby on Rails, etc) application on Kubernetes. This is because Kubernetes is not magical, and it's not even that new. It's a very nice abstraction layer on...
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