実践的なトレーニングで、クラウドのエキスパートになりましょう。

Google Cloud Run Serverless Workshop

Twelve years ago Lily started the Pet Theory chain of veterinary clinics, and has been expanding rapidly. Now, Pet Theory is experiencing some growing pains: their appointment scheduling system is not able to handle the increased load, customers aren't receiving lab results reliably through email and text,and veteranerians are spending more time with insurance companies than with their patients. Lily wants to build a cloud-based system that scales better than the legacy solution and doesn't require lots of ongoing maintenance. The team has decided to go with serverless technology. For the labs in the GCP Serverless Workshop: Pet Theory Quest, you will read through a fictitious business scenario in each lab and assist the characters in implementing a serverless solution.

Anthos: Service Mesh

This intermediate-level quest is unique among Qwiklabs quests. These labs have been curated to give operators hands-on practice with Anthos—a new, open application modernization platform on GCP. Anthos enables you to build and manage modern hybrid applications. Tasks include: installing service mesh, collecting telemetry, and securing your microservices with service mesh policies. This quest is composed of labs targeted to teach you everything you need to know to introduce service mesh, and Anthos, into your next hybrid cloud project.

DevOps Essentials

DevOps is an organizational and cultural movement that aims to increase software delivery velocity, improve service reliability, and build shared ownership among software stakeholders. In this quest you will learn how to use Google Cloud to improve the speed, stability, availability, and security of your software delivery capability. DevOps Research and Assessment has joined Google Cloud. How does your team measure up? Take this five multiple-choice question quiz and find out!

Applied Data: Blockchain

Blockchain and related technologies such as distributed ledger and distributed apps are becoming new value drivers and solution priorities in many industries. In this Quest you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. This Quest brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. As this Quest utilizes advanced SQL in BigQuery, we've added a SQL-in-BigQuery refresher lab at the start. The final lab is an advanced challenge-style lab in which there are elements where you are not provided the answer but must solve it for yourself.

Cloud Healthcare API

Cloud Healthcare API bridges the gap between care systems and applications built on Google Cloud. By supporting standards-based data formats and protocols of existing healthcare technologies, Cloud Healthcare API connects your data to advanced Google Cloud capabilities, including streaming data processing with Cloud Dataflow, scalable analytics with BigQuery, and machine learning with Cloud Machine Learning Engine. In this Quest you will use the Cloud Healthcare API to ingest and process data in the industry standard FHIR, HL7v2 and DICOM formats, train a TensorFlow model for prediction with FHIR data, and also gain practice with de-identification of datasets.

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.

Optimizing Your GCP Costs

This is the second Quest in a two-part series on GCP billing and cost management essentials. This Quest is most suitable for those in a Finance and/or IT related role responsible for optimizing their organization’s cloud infrastructure. Here you'll learn several ways to control and optimize your GCP costs, including setting up budgets and alerts, managing quota limits, and taking advantage of committed use discounts. In the hands-on labs, you’ll practice using various tools to control and optimize your GCP costs or to influence your technology teams to apply the cost optimization best practices.

注目の投稿

Qwiklabs と Google Cloud Platform の概要

この最初のハンズオンラボでは、Qwiklabs と Google Cloud Platform Console にアクセスし、GCP の基本機能（プロジェクト、リソース、IAM ユーザー、役割、権限、API、Cloud Shell）を使用します。

Exploring Google Ngrams with Amazon EMR（日本語版）

このラボでは、ビッグデータを処理するための Amazon Elastic MapReduce (EMR) クラスターを起動し、Hive と SQL スタイルのクエリを併用してデータを分析する方法について説明します。Amazon EMR を使用して小さな Hadoop クラスターを作成し、Amazon S3 に保存されているデータに対してインタラクティブな Hive クエリを実行できるようにします。また、Hive を使用して有用性の高い方法でデータを標準化します。さらに、有益な結果でテーブルを生成し、それを Amazon S3 に保存して、クラスター上で実行される他のジョブに再利用することにも取り組みます。

Introduction to AWS Identity and Access Management (IAM) (日本語版)

このラボでは、AWS Identity and Access Management (IAM) を使用して AWS のサービスへのアクセス権限を管理する方法について説明します。グループへのユーザー追加、パスワード管理、IAM で作成されたユーザーを使ったログイン、および特定のサービスへのアクセスに対し IAM ポリシーの及ぼす効果について、ステップごとに演習します。

仮想マシンの作成

このハンズオンラボでは、Google Compute Engine の仮想マシンを作成する方法や、ゾーン、リージョン、マシンタイプについて学習します。内容を紹介したプレビュー動画 Create a Virtual Machine, GCP Essentials もご覧ください。

注目

Serverless Architectures with Amazon DynamoDB and Amazon Kinesis Streams with AWS Lambda (

このラボは、2 つのパートから構成されています。ラボの最初のパートでは、設計図から Lambda 関数を作成し、Amazon Kinesis ストリームを作成します。その後、ストリームのデータを使用して関数をトリガーし、Amazon CloudWatch でプロセスをモニタリングします。ラボの 2 つ目のパートでは、Amazon DynamoDB、そのストリーム機能、および AWS Lambda を使用したイベント駆動型プログラミングの基礎を学びます。また、DynamoDB ストリームと Lam

注目

Applied Machine Learning: Building Models for an Amazon Use Case

In this lab you will clean data, conduct feature engineering, compare algorithms, and get a firsthand look at how Amazon employees working with machine learning approach ML pipelines.

注目

AWS Tools for Windows PowerShell: Getting Started

This lab will take you through the steps to set up and use the AWS Tools for Windows PowerShell. This toolkit enables you to manage your AWS resources the same way you use Windows PowerShell to manage your on-premise Windows environment. You will examine the various authentication types that can be used with AWS Tools for Windows PowerShell. At the end of this lab, you will use AWS Tools for Windows PowerShell to write your own self-healing PowerShell scripts.

高評価

Hardening Default GKE Cluster Configurations

This lab demonstrates some of the security concerns of a default GKE cluster configuration and the corresponding hardening measures to prevent multiple paths of pod escape and cluster privilege escalation

高評価

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.

高評価