Expert 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.
Prerequisites
This 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.Quest Outline
Managing Deployments Using Kubernetes Engine
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.
Build a Slack Bot with Node.js on Kubernetes
In this lab you'll learn how to build and run a Slack Bot with Google Cloud Platform uses Kubernetes Engine, a hosted version of Kubernetes. Watch the short video Build a Slack Bot with Node.js on Kubernetes
Using Kubernetes Engine to Deploy Apps with Regional Persistent Disks
In this lab you will learn how to configure a highly available application by deploying WordPress using regional persistent disks on Kubernetes Engine.
NGINX Ingress Controller on Google Kubernetes Engine
Hands-on lab to deploy the NGINX Ingress Controller on Google Kubernetes Engine.
Distributed Load Testing Using Kubernetes
Lab has instructions to conduct distributed load testing with Kubernetes, which includes a sample web application, Docker image, and Kubernetes controllers/services.
Running Dedicated Game Servers in Google Kubernetes Engine
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.
Awwvision: Cloud Vision API from a Kubernetes Cluster
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.
Running a MongoDB Database in Kubernetes with StatefulSets
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.
Kubeflow End to End
In this hands-on lab, you will install Kubeflow on an empty Kubernetes Engine cluster and use it to train and serve a sequence-to-sequence model using TensorFlow, Keras, and SeldonIO.
Deploy a Web App on GKE with HTTPS Redirect using Lets Encrypt
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.
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