Accelerate 2019: Game, Tech Track & Breakout Labs
Advanced 33 Steps 33h 48m 104 Credits
This Quest is composed of the hands-on labs used in the Google Cloud SCORE! Game as well as the rest of the non-game labs corresponding to the Tech Tracks and Breakouts from the Accelerate 2019 Sales Kickoff. These labs, especially the non-game labs do not all work completely and will not all be maintained-- many of the GCP services and features used in these labs are in early alpha or beta status and are both unreliable and subject to change at any time.
PrerequisitesAttendance at Tech Track Breakouts corresponding to the lab you are interested in.
ObjectivesGaining credibility and hands-on experience with Google Cloud Platform features.
AudienceTech role attendees at Accelerate 2019.
Starting with an existing SOAP service, you'll import it into Apigee and apply some basic security, traffic management, and message transformation policies.
warning Developing with TPUs in Jupyter
In this lab, you will learn to use a Cloud TPU to accelerate specific TensorFlow machine learning workloads on Compute Engine.
warning Training a ResNet Image Classifier from Scratch with TPUs on Cloud MLE
In this lab, you will train a state-of-the-art image classification model on your own data using Google's Cloud TPUs from CloudShell.
In this lab, you'll learn important connectivity, performance, testing, and troubleshooting techniques in the Google Cloud Networking insfrastructure environment.
In this lab, you create two managed instance groups in the same region. Then, you configure an Internal Load Balancer with the instances groups as the backends.
warning Helmsman GKE Networking
This demo strives to simplify the best practices for exposing cluster services to other clusters and establishing network links between Kubernetes Engine clusters running in separate VPCs.
warning Responding to Stackdriver Messages with Cloud Functions
In this lab you will learn how to use Cloud Functions to do lightweight processing of Stackdriver Logging messages
warning Serverless Compute: Microservices with Cloud Functions
In this lab you create a series of Cloud Functions that provide widget processing services
warning Healthcare API Overview
In this lab, you will discover and use basic functionalities of Cloud Healthcare API.
warning Creating and Deploying a Kubeflow Machine Learning Pipeline
In this lab, you will see how to create and deploy a Kubeflow Machine Learning Pipeline.
This lab shows you how to install and configure Istio on Kubernetes Engine, deploy an Istio-enabled microservices application, install Apigee Istio Mixer plugin which provides additional security, governance , and enforces authorization via Apigee.
In this lab you will learn how to leverage Apigee to transparently migrate application traffic from legacy backends to GCP-backed systems.
In this lab you will learn how to leverage Apigee Edge to integrate with Google Cloud Platform’s Stack Driver for message logging.
In this hands-on lab you create a Gmail Add-on that allows you to quickly change the labels of an email thread.
warning Troubleshooting Cloud Service Mesh
In this lab, you will walk through an example of how to troubleshoot a cloud service mesh using Stackdriver.
warning Getting Started with SAP HANA Express
SAP HANA is an in-memory data and application platform that makes real-time business a reality. Learn how to quickly and easily launch this significant industry platform on Google Cloud.
warning Overview of GKE On-Prem - Hybrid Platform Modernization #1
In this lab, you will walk through the experience of deploying the GKE Connect agent to a GKE On-Prem cluster and registering your cluster in your GCP account. Once setup, you will be able to see your cluster in the GCP Console and manage it.
In this lab, you will receive hands-on practice with the Google Drive (REST/HTTP) API. After enabling the API and obtaining the credentials needed, you will make an API request through an application that lists the first 100 files and folders in your Drive.
Record linking is the task of identifying similar or duplicated records in a database and forming a consolidated set. You will learn the process of finding similar objects in a small dataset to explain the nuances of the problem at hand.
Learn to use Google Dataflow to process real-time streaming data from a real-time real world historical data set, store the results in Google BigQuery, then use Google Data Studio to visualize real-time geospatial data.
The objective of this lab is to familiarize yourself with the specific capabilities of Stackdriver to monitor GKE cluster infrastructure, Istio, and applications deployed on this infrastructure.
warning Cloud Hero: Classic
Cloud Hero: Classic Challenge Lab
warning Deploying and Monitoring SAP S/4HANA on Google Cloud Platform
In this hands-on lab you will deploy and run a SAP S/4HANA solution and then configure basic Stackdriver Monitoring.
warning Datacenter Migration: Modernize
This lab will walk you through an optimization stage of a migration. In this lab you continue their migration journey from "Lift and Shift.". In this lab you migrate from MySQL on GCE to Cloud SQL (managed service).
warning Datacenter Migration: Lift and Shift
In this lab, you will perform a lift and shift virtual machine migration of an application and database server from one Google Cloud Platform region to another disparate region by leveraging Cloud Endure’s Live Migration tool.
warning Datacenter Migration: Assessment
This lab demonstrates a use of an assessment tool and how to enable it on a source environment on AWS. During the lab the students will get exposure to features and capabilities they are not familiar with.
warning Cloud Service Mesh: Provision and Monitoring
In this lab, you will walk through the experience of deploying a GKE cluster with Istio. Then you will setup up an example application and monitor it.
warning Migrating Data from Teradata to Google BigQuery
In this lab you will get hands-on practice with end-to-end migration from Teradata to Bigquery.
In this lab you will train a simple machine learning model for predicting helpdesk response time using BigQuery Machine Learning.
Cloud Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. In this lab you will explore the Cloud Dataprep UI to build a data transformation pipeline.
In this lab you will use a newly available ecommerce dataset to run some typical queries that businesses would want to know about their customers’ purchasing habits.
In this lab you will learn how to use Google App Maker to build a web app that is connected a database.