thinkdeep VS kubernetes

A complex codebase needs to be kept as simple as possible. In Predecos, one way this is done is through use of full-stack JavaScript, meaning developers only need experience with one language to maintain the codebase. Some differences exist in NodeJS when compared with browser-based JavaScript but those are minor compared with having to work in multiple languages. Front-end and back-end dependencies are also kept as uniform as possible. For example, Mocha and Chai are used in both the front-end and back-end meaning experience with said tools enables development in either. Developer setup and deployment should also be straightforward. Predecos uses Helm v3.8.2 to trigger one-click deployment of all the microservices. After doing this and setting a couple environment variables, one can easily start the front-end by using yarn run start then they are ready for development. This is also valuable when doing a deployment. When CircleCI went down, the impact was slight because automated deployment and manual deployment were very similar. All that was required was modifying the CircleCI config for the terminal, building the necessary docker images manually, pushing those images and running helm upgrade. A simple and fast mitigation that saved a lot of time. Diverging from simplicity was sometimes necessary though. Some may argue that use of Kubernetes makes the codebase difficult to understand by virtue of its high learning curve. However, the stability, reliability, documentation and wide-spread adoption of Kubernetes made it hard to resist. Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure and DigitalOcean all have Kubernetes offerings. This means applications developed on Kubernetes are largely portable from one cloud provider to another. Predecos uses DigitalOcean’s managed Kubernetes product because it is low-cost when compared to the other options. Should the desire for features such as, for example, encryption at rest arise, one option would be to migrate to Google Kubernetes Engine (GKE). Some modifications would be necessary but because Predecos is in a proof-of-concept (PoC) state and minimal data has been stored that migration would likely be simple.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

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).