为了让您跳出模拟环境，在实践中来了解云，我们特为您提供访问“Google Cloud Platform 和 Amazon Web Services”的临时凭据。我们力求覆盖每个人的学习需求。无论您是初学者还是专家，感兴趣的是时长半小时的单个实验还是跨度达数日的课程，都可以通过自定节奏的培训或有讲师指导的培训来探索机器学习、安全保护、基础架构、应用开发等各种主题。
在此入门级挑战任务中，您可以使用 Google Cloud Platform 的基本工具和服务，开展真枪实弹的操作实训。“GCP 基本功能”是我们为 Google Cloud 学员推荐的第一项挑战任务。云知识储备微乎其微甚至零基础？不用担心！这项挑战任务会为您提供真枪实弹的实操经验，助您快速上手 GCP 项目。无论是要编写 Cloud Shell 命令还是部署您的第一台虚拟机，亦或是通过负载平衡机制或在 Kubernetes Engine 上运行应用，都可以通过“GCP 基本功能”了解该平台的基本功能之精要。点此观看 1 分钟视频，了解每个实验涉及的主要概念。
如果您是新手云开发人员，希望在GCP Essentials之外寻求动手实践，那么此任务适合您。通过深入研究Cloud Storage和其他关键应用程序服务（如Stackdriver和Cloud Functions）的实验室，您将获得实践经验。通过执行此任务，您将开发适用于任何GCP计划的宝贵技能。 1分钟的视频向您介绍这些实验室的关键概念。
Kubernetes in 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. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, finish the additional at the end of the
Obtain a competitive advantage through DevOps. 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!
This fundamental-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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.
A Tour of Qwiklabs and the Google Cloud Platform
In this first hands-on lab you will access Qwiklabs and the Google Cloud Platform Console and use the basic GCP features: Projects, Resources, IAM Users, Roles, Permissions, APIs, and Cloud Shell.
Creating a Virtual Machine
In this hands-on lab, you’ll learn how to create a Google Compute Engine virtual machine and understand zones, regions, and machine types. To preview, watch the short video Create a Virtual Machine, GCP Essentials.
Getting Started with Cloud Shell & gcloud
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. For a preview, watch the short video Get Started with Cloud Shell, GCP Essentials.
Kubernetes Engine: Qwik Start
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.
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.
Automating DevOps Workflows with GitLab and Terraform
In this lab, you will learn how to use GitLab and Terraform to automate devops workflows.
Managing IoT Sensor Data with Amazon ElastiCache for Redis
In this lab, you will see how AWS IoT makes it easy to use AWS services like AWS Lambda and Amazon ElastiCache Service to build IoT applications that gather, process, and analyze data generated by connected devices (in this case an emulator), without having to manage any infrastructure. The goal of capturing time series data from IoT devices such as sensors is to learn from observations and apply that learning or analytics to improve upon some experience or to help predict the next event, given previous observations. Here we'll simulate temperature sensor readings and capture, then ingest and analyze that data with Amazon ElastiCache for Redis. Prerequisites: To successfully complete this lab, you should be familiar with the basics of Elasticache and AWS Internet-Of-Things from taking the Introduction to Amazon Elasticache lab and the Introduction to Internet-Of-Things lab. Further experience with SQL and command-line operations will also be useful. For the lab to function as written, please DO NOT change the auto assigned region.
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
Learning TensorFlow: the Hello World of Machine Learning
In this lab, you learn the basic ‘Hello World' of machine learning. Instead of programming explicit rules in a language such as Java or C++, you build a system that is trained on data to infer the rules that determine a relationship between numbers.