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.
In this hands-on lab we take a look through some of the information provided by the Stackdriver Monitoring tools, and teach some of the concepts you will need to know to take advantage of Stackdriver Monitoring effectively.
Google Compute Engine lets you create and run virtual machines on Google infrastructure. In this lab you will learn how to create a Windows Server instance in the Google Compute Engine and access it with RDP.
In this lab you will learn how to start a managed Spark/Hadoop cluster using Google Cloud Dataproc, submit a sample Spark job, and shut down your cluster using the command line.
This lab shows you how to use Google Cloud Platform Console to create a Cloud IoT Core device registry and register a device. It also shows you how to run a sample to connect a device and publish device telemetry events.
This hands-on lab demonstrates how to build a Docker image and push it to Google Container Registry.
In this hands-on lab, you will learn how to connect to computing resources hosted on Google Cloud Platform via the web. You will also learn how to use Cloud Shell and the Cloud SDK gcloud command.
This lab shows you how to create a Google Cloud Dataproc cluster, run a simple Apache Spark job in the cluster, then modify the number of workers in the cluster, all from the gcloud Command Line.
In this lab you’ll learn how to classify text into categories using the Natural Language API
This hands-on lab shows you how to create a small App Engine application that displays a short message. Watch the short video Build Apps at Scale with Google App Engine.
In this lab you will learn how to build a custom interactive dashboard application on Google Cloud Platform (GCP) by using the Bokeh library to visualize data from publicly available Google BigQuery datasets.
In this hands-on lab, you’ll learn how to create a Google Compute Engine virtual machine and understand zones, regions, and machine types.
This page shows you how to use the Google Cloud Platform Console to create a Google Cloud Dataproc cluster, run a simple Apache Spark job in the cluster, then modify the number of workers in the cluster.
In this lab you will learn how to create secure, high-throughput VPN and test the speed.
This lab demonstrates how to create an HTTP(S) load balanced that forwards traffic to instances in two different regions.
In this lab, you learn how to turn your ASP.NET Core code into a Docker container image, and then run that image as a replicated application running on Kubernetes on Google Container Engine (GKE).
In this lab, you will learn how to use App Engine Flexible with Python’s Flask framework. You’ll deploy a web application that allows users to upload photos of people’s faces and do simple facial recognition with the Cloud Vision API.
In this lab, you'll tour some of the unique Stackdriver features enabling you to monitor and troubleshoot your applications with production debugging, API call tracing, and dynamically-added logpoints.
This lab creates a basic Deployment Manager (DM) configuration for deploying resources in Google Cloud Platform.
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
Learn how to install and configure Istio, an open source framework for connecting, securing, and managing microservices, on Google Kubernetes Engine. You will also deploy an Istio-enabled multi-service applicaiton.
Cloud Bigtable is Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail. In this lab you'll use Bigtable with the cbt command line.
This lab shows you how to monitor a Google Compute Engine virtual machine (VM) instance with Stackdriver. Watch the short video Monitor Health of All Your Cloud Apps with Google Stackdriver.
In this hands-on lab, you'll learn how setup both network load balancers and HTTP load balancers for your application running in Google Compute Engine virtual machines.
In this lab, you will learn how to use Apache Spark on Cloud Dataproc to distribute a computationally intensive image processing task onto a cluster of machines.
The Cloud Natural Language API lets you extract entities and perform sentiment and syntactic analysis on text. Watch the short video Gain Valuable Insights from Text with Cloud Natural Language.
This lab shows you how to build a logbook application using Node.js as the frontend and MySQL on the backend. You'll also create a network load balancer and an autoscaler to watch the frontend instances and scale when necessary.
This lab will serve as an introduction to Kubeflow, an open-source project which aims to make running ML workloads on Kubernetes simple, portable and scalable.
In this lab, you’ll build a store locator web application for the NYC (New York City) Subway Station data set.
In this lab you analyze a large (137 million rows) natality dataset using Google BigQuery and Cloud Datalab. This lab is part of a series of labs on processing scientific data.
In this Quest, the key building blocks of Google Cloud Platform Security are presented in short hands-on lab tutorial format. Beginning with Identity and Access Management (IAM) the student will learn roles, permissions, and service accounts. Then key aspects of networking security are demonstrated, including setting up VPCs and VPNs. Finally Cloud Tools for privacy and security are presented along with a lab teaching how to set up a private Kubernetes Cluster.
In this Quest, you will learn common solution patterns compiled by Google's own Solutions Architects. Each hands-on experience presents an application of multiple Google Cloud services to accomplish a common technical use case. These labs are at Advanced or Expert level, and students should have earned at least 2-3 badges prior to attempting these labs. Suggested prerequisites: GCP Essentials Quest, Networking in the Google Cloud Quest, and Kubernetes in the Google Cloud Quest.
In this Quest, you will learn common solution patterns compiled by Google's own Solutions Architects. Each hands-on experience presents an application of multiple Google Cloud services to accomplish a common technical use case. These labs are mostly at the Advanced or Expert level, and students should have earned at least 2-3 badges prior to attempting these labs. Suggested prerequisites: GCP Essentials Quest, Data Engineering Quest, and the Scientific Data Processing Quest.
In this Quest, students will get hands-on experience using the suite of tools in Google Stackdriver. This comprehensive toolset covers monitoring, logging, and application performance analysis. Labs cover uptime checks, alerting policies, monitoring Cloud Functions, creating Logs-based metrics, and instrumenting applications to send performance data to custom dashboards.
In this Quest, students will get hands-on experience from introductory Docker tools through using Kubernetes to manage application packaging and launch, through sophisticated Continuous Delivery deployment management scenarios.
This series of self-paced labs introduces the student to Application Development for Google Cloud Platform. You will learn how to develop cloud-based applications using Google App Engine, Google Cloud Datastore, and other GCP services such as Cloud Spanner. Finally you will integrate the services and deploy.to both App Engine and to a Kubernetes Cluster in Google Kubernetes Engine. The programming language used in this Quest is Python, and significant Python familiarity is a prerequisite, but no original coding is required.
This series of self-paced labs introduces the student to Application Development for Google Cloud Platform. You will learn how to develop cloud-based applications using Google App Engine, Google Cloud Datastore, and other GCP services such as Cloud Spanner. Finally you will integrate the services and deploy.to both App Engine and to a Kubernetes Cluster in Google Kubernetes Engine. he programming language used in this Quest is Java, and significant Java familiarity is a prerequisite, but no original coding is required.
In this Quest, you will gain hands-on experience with Google Cloud Platform tools and services by exploring and visualizing public domain scientific data sets. You will get hands-on practice with the core tools for manipulating and transforming data originating from the natural and social sciences. Learners should be experienced with data science concepts and be comfortable with working in the Google Cloud Platform.
In this Quest, you will learn to deploy several examples of web-based applications. Each example presents a different use case, solution, or integration pattern with other services. The Ruby programming language is used in most of these examples. Suggested prerequisite: Baseline Deploy and Develop Quest.
In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Deployment Manager, the Google Cloud service that allows you to work with "Infrastructure as code". In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.
In this Quest, you will get hands-on practice with the core tools for deploying and launching custom applications in Google App Engine and Google Compute Engine. The demonstration applications in this Quest cover four programming languages/frameworks: Ruby on Rails, Python/Flask, Node.js, and ASP.NET Core.
Learn to run your Windows workloads on the Google Cloud.
In this Qwiklab, you set up a redundant pair of Windows Domain Controllers (DC) with AD using a new Virtual Private Cloud (VPC) network and multiple subnets on Google Cloud Platform (GCP).
In this lab, you’ll learn how to use the Firebase platform to easily create Web applications and implement and deploy a chat client.
Google Kubernetes Engine provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. This hands-on lab shows you how deploy a containerized application with Kubernetes Engine. Watch the short video Manage Containerized Apps with Kubernetes Engine.
Data Studio lets you create dynamic, visually compelling reports and dashboards
This lab describes how to deploy an autoscaling Compute Engine instance group that is automatically scaled using a custom Stackdriver metric
This lab will show you how to install and run an object detection application. The application uses TensorFlow and other public API libraries to detect multiple objects in an uploaded image.