Expert 10 Steps 8h 55m 72 Credits
Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 9 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
PrerequisitesThis Quest builds on an understanding of Kubernetes and the Google Kubernetes Engine, and extends basic GKE operations into integrations with other GCP services. It is recommended that the student has earned the Badge for the Cloud Architecture Quest and the Kubernetes in the Google Cloud Quest before beginning.
Dev Ops best practices make use of multiple deployments to manage application deployment scenarios. This lab provides practice in scaling and managing containers to accomplish common scenarios where multiple heterogeneous deployments are used.
In this lab you will learn how to configure a highly available application by deploying WordPress using regional persistent disks on Kubernetes Engine.
Hands-on lab to deploy the NGINX Ingress Controller on Google Kubernetes Engine.
Lab has instructions to conduct distributed load testing with Kubernetes, which includes a sample web application, Docker image, and Kubernetes controllers/services.
This lab will show you how to use an expandable architecture for running a real-time, session-based multiplayer dedicated game server using Kubernetes on Google Container Engine.
This hands-on lab uses Kubernetes and Cloud Vision API to create an example of how to use the Vision API to classify (label) images from Reddit’s /r/aww subreddit and display the labelled results in a web app.
Containers are becoming a popular way to run and scale applications across multiple cloud providers or on both cloud and on-premise hardware. This lab provides a quick introduction to running a MongoDB database on Kubernetes Engine using Docker.
In this hands-on lab, you will install Kubeflow on an existing Google Kubernetes Engine cluster and use it to train and serve a sequence-to-sequence model using Tensorflow, Keras, and SeldonIO.
This lab shows you how to deploy a web app with a browser-trusted TLS certificate. You also deploy an HTTPS redirect on GKE using Let's Encrypt, NGINX Ingress, and Cloud Endpoints.