Travis CI.com VS kubernetes

Travis CI: This hosted CI/CD service can seamlessly integrate with code hosting platforms like GitHub.

TRAVIS: With the help of Travis CI, you can easily synchronize your GITHUB projects. Travis offers more language support than circleCI and also you can run test on linux and mac OS at the same time. For more info: https://travis-ci.org/

Automation is a critical tool for improving DevOps efficiency. Many cloud platforms offer enhanced automation solutions for DevOps activities, such as CI/CD. CircleCI, Jenkins, GitLab, and Travis CI are all examples of such tools used for continuous integration. These technologies provide uniformity and speed while requiring minimal human intervention.

And finally, the engineers need to be able to easily deploy to Kubernetes, which can either be done with the same tools or with specialized CI/CD tools, such as Jenkins, Circle CI, or Travis CI.

To deploy an application to production, more complex continuous integration and deployment solutions exist. Since Kubernetes is so common now that almost all CI/CD tools support it, it does not really matter if these solutions are particularly specialized on Kubernetes or not. You should rather compare different solutions again and see which best fits your needs. A good starting point are these tools: Jenkins, Codefresh, Travis CI, and Circle CI.

And it became quite a good conversation like, well, I wish that it would also update my GitHub Actions tree because of my Travis CI tree because I wish it did this, I wish it did that. I think the biggest users were the WG, the browser rendering engine people. They had some requirements they couldn't use until they were fixed. So we had a really good conversation there. But yeah, tech is never the hard part; it's always the people.

This tool is written in Golang and still used travis-ci as CI. Furthermore, some parts of the release process were still manually, such as uploading the assets to a GitHub release and generating the release notes. We wanted to have this automated.

You should use a service like Travis CI. Much easier than the route you are taking.

https://fastlane.tools https://danger.systems https://www.sonarqube.org https://codemagic.io/ https://travis-ci.org

Datree has really good integration with CicleCi, TravisCi, GitHub Actions, GitLab also.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.