February 12, 2018
When it comes to building Docker containers, you should always strive for smaller images. Images that share layers and are smaller in size are quicker to transfer and deploy.
But how do you keep the size under control when every
RUN statement creates a new layer, and you need intermediate artefacts before the image is ready?
April 25, 2018
Laravel is an excellent framework for developing PHP applications. Whether you need to prototype a new idea, develop an MVP (Minimum Viable Product) or release a full-fledged enterprise system, Laravel facilitates all of the development tasks and workflows. In this article, I’ll explain how to deal with the simple requirement of running a Laravel application as a local Kubernetes set up.
May 15, 2018
When you deploy an application in Kubernetes, your code ends up running on one or more worker nodes. A node may be a physical machine or VM such as AWS EC2 or Google Compute Engine and having several of them means you can run and scale your application across instances efficiently. When there is an incoming request, the cluster routes the traffic to one of the nodes using a network proxy. But what happens when network proxy crashes? Does the cluster still work? Can Kubernetes recover from the failure?
June 05, 2018
Getting started with Docker and Kubernetes on Windows can be daunting when you don't know where to begin. And it doesn't help that installing the software isn't exactly a walk in the park. In fact, you should already be a Docker and Kubernetes expert to navigate the options on how to install them. But don't worry! If you're just starting your journey with containers and Kubernetes on Windows this article is for you. You'll learn how to make the right choices when it comes to setting up your development environment on Windows.
July 11, 2018
When you design and build applications at scale, you deal with two significant challenges: scalability and robustness. You should design your service so that even if it is subject to intermittent heavy loads, it continues to operate reliably. But how do you build such applications? And how do you deploy an application that scales dynamically? Kubernetes has a feature called autoscaler where instances of your applications are increased or decreased automatically based on metrics that you define.
September 04, 2018
In the last few years, the industry has experienced a shift towards developing smaller and more focused applications. Smaller services are excellent from a product and development perspective: they are quicker to deploy, easier to iterate on and can handle failure gracefully. But how does that cultural shift impact the infrastructure? The current practices don't fit the paradigm well, and you might end up paying the extra price in your cloud bill at the end of the month.
November 06, 2018
Spot Instances are unused servers that are available for less than the regular price. Therefore, you can significantly save on your infrastructure costs. It does come with a price, though. Your cloud provider can take away your spot instance at any time, and give to another client who has requested it at a standard cost. How can you save money, but work around disappearing servers? Learn how you can leverage Kubernetes to self-heal your infrastructure and cut costs with Spot Instances.
December 04, 2018
Solar panels are getting cheaper, and are becoming an economically viable source of renewable energy in many parts of the world. For solar panels to operate efficiently, they need to be kept clean and pointed at an optimal angle to the sun that balances power generation and prevents overheating. An embedded computer is in charge of monitoring metrics and driving the actuators. But when you have thousands of solar panels and embedded computers how do you orchestrate software updates, monitor uptime and secure communications?
January 09, 2019
One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments, but the process takes time away from focussing on training and developing the models. But what if you could outsource all of the non-data science to someone else while still retaining control? In this article, you will explore how you can leverage Kubernetes, Tensorflow and Kubeflow to scale your models without having to worry about scaling the infrastructure.