In this article, we’ll look at two container management solutions — Kubernetes and Amazon Elastic Container Service (ECS) — from a perspective that makes sense for aspiring and current data scientists.
When you’re on your way to having a data science career, you’ll undoubtedly encounter opportunities to use container management solutions. Here, we’ll look at two solutions — Kubernetes and Amazon Elastic Container Service (ECS) — from a perspective that makes sense for aspiring and current data scientists.
If you’re interested in building and using machine learning models as part of your work as a data scientist, tutorials exist that walk you through doing that on both platforms. Amazon provides a step-by-step walkthrough aimed at data scientists.
Then, there is a machine learning toolkit for Kubernetes users called Kubeflow. It’s an open-source, portable and scalable solution. People can apply it to any new or existing Kubernetes deployments.
As you look at comparisons between Kubernetes and ECS, don’t be surprised to find details about availability zones and overall reliability. Choosing a dependable service can help you avoid outages that could temporarily derail your data science projects.
ECS runs in 69 availability zones and 22 regions. Also, ECS falls under the Amazon Web Services (AWS) umbrella. That means it guarantees an uptime rate of at least 99.99%.
Kubernetes emphasizes reliability with an approach that spreads Kubernetes pods among nodes to make them more tolerant of application failures. Moreover, the high availability of Kubernetes extends to both the infrastructure and application level.
Moreover, with the release of Kubernetes 1.2 came support for running single clusters in multiple availability zones offered by cloud providers. However, the selected zones must be in the same region and provided by the same cloud service.
That Kubernetes version offered multiple zone selection automatically to AWS and Google Compute Engine (GCE) users. However, applying the appropriate labels to nodes and volumes creates support for additional cloud services.
Our original Kubernetes tool list was so popular that we've curated another great list of tools to help you improve your functionality with the platform.
This article explains how you can leverage Kubernetes to reduce multi cloud complexities and improve stability, scalability, and velocity.
Get Hands-on experience on Kubernetes and the best comparison of Kubernetes over the DevOps at your place at Kubernetes training
Get Hands-on experience on Kubernetes and the best comparison of Kubernetes over the DevOps at your place at Kubernetes training
Microsoft announced the general availability of Bridge to Kubernetes, formerly known as Local Process with Kubernetes.