Architecture Development Load Balancer Open Source 
All Things Distributed
DECEMBER 1, 2016
At AWS, we want to be the Q for developers, giving them the super-powered tools and services with deep features in the Cloud. In the hands of builders, the impact of these services has been to completely transform the way applications are developed, debugged, and delivered to customers. Transformation in Data.
Galido
APRIL 27, 2020
However, even though the project was promising, in 2015, Google released this tool as an open-source project. It is also possible to copy containers to development, test, integration, and live environments quickly and reliably. You cannot possibly deny that Kubernetes is built on a very mature and proven architecture.
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Scott Lowe
JANUARY 21, 2022
Nick Schmidt talks about using GitOps with the NSX Advanced Load Balancer. Chris Evans revisits the discussion regarding Arm processor architectures in the public cloud. The story of a developer deliberately polluting their open source projects—as outlined here for the “colors.js”
Scott Lowe
OCTOBER 14, 2017
Romain Decker has an “under the hood” look at the VMware NSX load balancer. I’ll keep an eye open for links to include next time around. This graphical summary of the AWS Application Load Balancer (ALB) is pretty handy. Servers/Hardware. Nothing this time (sorry!). Operating Systems/Applications.
Linux Academy
MARCH 28, 2019
The popularity of agile development, continuous integration, and continuous delivery has brought levels of automation that rival anything preciously known. The shift-left mentality has given development teams and product owners far more control over their release management. Automated Kubernetes Deployments.
Scott Lowe
JUNE 23, 2015
Porting it back to development wasn’t the right approach. This led BI to Fig (now Docker Compose), which led to a decrease in the time it took to get a development environment up and running. Before Docker, the workflow was developers to GitHub to Jenkins, which then pushed to AWS in production. On-premise registry. Networking.
Dataconomy
APRIL 28, 2023
With the increasing demand for machine learning deployment, various tools and platforms have emerged to help data scientists and developers deploy their models quickly and efficiently. From cloud-based services to open-source frameworks, these tools offer a range of features and functionalities to cater to different deployment needs.
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