Expert 10 Steps 8h 55m 72 Créditos
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.
En este lab, practicará el escalamiento y la administración de contenedores en una serie de situaciones comunes en las que se emplean varias implementaciones heterogéneas.
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.
Este lab le brinda una introducción rápida a la ejecución de una base de datos de MongoDB en Kubernetes Engine con Docker.
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.
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.