Become a cloud expert with hands-on training.

OK Google: Build Interactive Apps with Google Assistant

With Google Assistant expected to be part of over a billion consumer devices by the end of this year, this Quest teaches you how to build practical Google Assistant applications integrated with Google Cloud services via APIs. Example apps will use the Dialogflow conversational suite and the Actions and Cloud Functions frameworks. Over the course of 5 labs, you will build 5 different applications that explore useful and fun tools you can extend on your own. No hardware required -- these labs use the cloud-based Google Assistant simulator environment for developing and testing -- but if you do have your own device, such as a Google Home or a Google Hub, additional instructions are provided on how to deploy your apps to your own hardware.

NCAA® March Madness®: Bracketology with Google Cloud

In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.

App Modernization with Apigee

Apigee enables you to create APIs and manage them for the benefit of other developers who might need to use your software. Apigee Edge enables you to quickly expose backend services as APIs. These "API Products" offer different capabilities and levels of service, with consumption managed by Apigee. Istio is an open source framework for connecting, securing, and managing microservices, especially services that are hosted in a Kubernetes cluster. This Quest of hands-on labs gives you practice in using Apigee for API creation and management functionality when you decide to modernize an application backend on Google Kubernetes Engine and an Istio based service mesh.

Kubernetes in the Google Cloud

Kubernetes is the most popular container orchestration system and the Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in the Google Cloud. In this advanced-level quest, you will get hands-on practice configuring Docker images and containers, and deploying fully-fledged Kubernetes Engine applications. This quest will teach you the practical skills needed for integrating container orchestration into your own workflow.

Challenge: GCP Architecture

This quest of "Challenge Labs" gives the student preparing for the Google Cloud Certified Professional Cloud Architect certification hands-on practice with common business/technology solutions using GCP architectures. Challenge Labs do not provide the "cookbook" steps, but require solutions to be built with minimal guidance, across many GCP technologies. All labs have activity tracking and in order to earn this badge you must score 100% in each lab. This quest is not easy and will put your GCP technology skills to the test. Be aware that while practice with these labs will increase your knowledge and abilities, we recommend additional study, experience, and background in cloud architecture to prepare for this certification.

G Suite: Integrations

This Quest of hands-on labs demonstrates the power of integrating Google Cloud Platform services and tools with G Suite applications. With integration technologies such as App Script and the Clasp Command Line environment, you will create and publish web apps and add-ons for G Suite products: Sheets, Docs, Forms, and Slides. With App Maker you will build a ready-to-use app that has a Google Cloud SQL Database, Google Maps integration, and a Mobile Responsive Design. Other labs create direct connections to GCP data sources-- using the BigQuery API, Sheets, and Slides to collect, analyze and present data.

Networking in the Google Cloud

Networking is a principle theme of cloud computing—it’s the underlying structure of GCP and it’s what connects all your resources and services to one another. This fundamental-level quest will cover essential GCP networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Networking in the Google Cloud will give you the practical experience needed so you can start building robust networks right away.

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Introduction to Amazon EC2 Auto Scaling

This lab provides the basic hands-on experience of Amazon EC2 Auto Scaling -- setting up Auto Scaling to automatically launch compute instances in response to conditions that you specify. You will use Auto Scaling via the AWS console to create the basic infrastructure of a Launch Configuration and an Auto Scaling group. You will test the configuration by terminating a running instance and viewing the results as Auto Scaling responds by scaling up and starting another instance.

For the lab to function as written, please DO NOT change the auto assigned region.

Exploring Google Ngrams with Amazon EMR

This lab demonstrates how to launch an Amazon Elastic MapReduce (EMR) cluster for Big Data processing and use Hive with SQL-style queries to analyze data. You will create a Hadoop cluster using Amazon EMR which will allow to run interactive Hive queries against data stored in Amazon S3. You will use Hive to normalize the data in a more useful way, and you will run queries to analyze the data.

Amazon Simple Storage Service (S3) 简介

本指南旨在向您介绍如何使用 AWS 管理控制台操作 Amazon Simple Storage Service。

AWS Identity and Access Management (IAM) 简介

本实验向您展示了如何使用 AWS Identity and Access Management (IAM) 来管理对 AWS 服务的访问和权限。练习以下步骤：向组中添加用户、管理密码、以 IAM 创建的用户身份登录和查看 IAM 策略对访问特定服务的影响。

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Integrating Machine Learning APIs

In this hands-on lab explore the Vision, Speech-to-Text, Translation, and Natural Language APIs and use the APIs to analyse audio recordings and map them to relevant images.

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Real Time Machine Learning with Google Cloud ML

Using Cloud DataProc running on a Hadoop cluster you will analyse a data set using Bayes Classification.

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Machine Learning with TensorFlow

In this lab you will learn how to use Google Cloud Machine Learning and Tensorflow to develop and evaluate prediction models using machine learning.

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Bracketology with Google Machine Learning

In this lab you use Machine Learning (ML) to analyze the public NCAA dataset and predict NCAA tournament brackets.

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Video on Demand with AWS Elemental MediaConvert

In this lab, you will utilize the AWS Elemental MediaConvert Service to convert input video into multiple output formats, combine multiple videos into one during the conversion process, add captions/watermarks to the videos, and work with ad insertion metadata.

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