I recently had an interesting project building a proof of concept for a cloud based platform. The PoC is composed of a few services, a message queue and a couple simple UIs. All the pieces are containerized and deployed on Kubernetes (GKE).  As a final task before sending this over to the client, I needed to expose the front end publicly. What seemed like a straightforward task turned out to be a bit more time consuming, partially due to the.

During DockerCon Copenhagen, Docker announced support and integration for Kubernetes, alongside Swarm. The first integration is in the Docker for Mac, where you can run now a 1 node Kubernetes cluster. This allows you to deploy apps with Docker-compose files to that local Kubernetes cluster via the docker cli. In this blogpost, I’ll cover what you need to know about this integration and how to make the most out of it.

After acquiring Stackdriver in 2014, Google worked hard to make it the default log aggregation and monitoring solution for Google Cloud Platform (GCP). The feature set of Stackdriver is pretty good for an out of box integrated solution for GCP. It is the equivalent of CloudWatch on AWS - doesn’t have all the bells and whistles but it’s relatively cheap, integrated and gets the job done.

Google is quietly releasing increasing amounts of projects dedicated to data science. One such project that was recently pointed out to me is called Kubeflow. In its essence, it is not terribly complicated. But when considered as part of the adoption of data science (and Google’s strategy), the project is of utmost importance.

If you are a little like me, chances are you get bored of ‘Hello World!’ examples quite quickly. When it comes to Kubernetes, the typical introductory article goes like this: