Google Cloud Next 2022 Hands-On Labs

Build and edit a Hello World Flutter application using a Code Server development enviornment.

This lab walks you through writing your first Flutter app compatible for Android, iOS, and web.

Design a basic frontend for a mobile app using Material Components for Flutter.

This lab demonstrates how to use Firebase Web authentication in a Flutter application.

Explore using Dart Function Frameworks in a Flutter application. Example based on this demo.

In this lab you will learn the basics of Dart. Understand how to work with variables, flow control and functions to start your journey with Dart.

In this lab you will learn how Dart is used with data structures. Understand the utility of Maps and Lists and how these are used to reference information.

阐述如何使用 Go 和 Cloud Run 构建 REST API

In this lab you will use Eventarc for Cloud Run to listen to events from Cloud Pub/Sub and Audit Logs. At the end of this lab you will be able to deliver events from various sources to Google Cloud sinks.

In this lab you learn how to set up API Gateway to manage and secure a Cloud Run backend service with gRPC.

在本实验中，您将在 Cloud Run 上构建一个用于转换 PDF 的 Web 应用。这是一个无服务器服务，会自动将存储在 Cloud Storage 中的文件转换为 PDF 并存储在单独的文件夹中。

In this lab you will deploy a website to Google Kubernetes Engine, scale it out to more instances, then deploy a new version using rolling updates.

Data validation is a critical step in data warehouse, database, or data lake migration.

在本实验中，您将学习如何实现 BigLake 表。

Send an image to the Cloud Vision API and have it identify objects, faces, and landmarks

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.

This lab will show you how to deploy a set of Cloud Run functions in order to process images and videos with the Cloud Vision API and Cloud Video Intelligence API.

In this lab, you will continue working on your Pigeon Travel chat agent and add context as well as setup fulfillment to lookup and store reservations entries in Firestore.

Use the Cloud Vision, Natural Language, and Translation APIs to capture text strings from images, recognize characters, and analyze and translate the text strings into other languages.

In this lab, you will learn how to build a binary classification model from tabular data using Vertex AI.

在本实验中，您将训练、调优、评估、解释一个 BigQuery ML XGBoost 模型，并使用该模型执行批量预测和在线预测。您将使用来自真实移动应用“Flood it!”的 Google Analytics 4 数据集，来确定用户重新使用该应用的可能性。您将使用 BigQuery ML 模型执行批量预测，将模型导出并部署到 Vertex AI 以进行在线预测。

在本实验中，您将训练一个 TensorFlow 模型并将其部署到 Vertex AI，以提供预测服务。请观看以下两个简短视频：Getting started with Notebooks for machine learning（面向机器学习的 Notebooks 入门指南）和 Faster model training and experimentation with Vertex AI（使用 Vertex AI 更快地进行模型训练和实验）。

In this lab, you will use TensorFlow's distribution strategies and the Vertex AI platform to train and deploy a custom TensorFlow image classification model to classify an image classification dataset.

In this lab you'll learn how to classify text into categories using the Natural Language API

借助 Cloud Natural Language API，您可以从一段文本中提取实体并执行情感和语法分析。在此动手实验中，您将了解如何使用 Cloud Natural Language API 从文本中提取实体和情感。

Learn how to use Managed Service for Prometheus in a self-deployed data collection mode. You can also utilize managed data collection as well.

This lab introduces you to the fundamentals of API Management with Google's Apigee API Management Platform. You will go through the API Lifecycle at a high level - from API design, API Security, API Consumption to API Analytics & Monitoring.

In this lab, you use an Apigee X threat protection policy and Cloud Armor to protect your APIs.

In this lab, you use the Apigee X provisioning wizard to create an Apigee X evaluation organization (org).

In this lab, you add a new environment and environment group to an Apigee X evaluation org.

In this lab, you enable a NAT IP address for the runtime instance. This static IP address is used when calling from Apigee X to backend services.

Use Migrate for Compute Engine to migrate an EC2 instance from AWS to Compute Engine on Google Cloud and verify the migration

In this lab, you migrate MySQL data from an Amazon RDS instance for MySQL to Cloud SQL for MySQL using a one-time Database Migration Service job and an IP allowlist for connectivity. After you create and run the migration job,...

In this hands-on lab, you will learn why Google Cloud is the cleanest cloud in the industry by exploring and utilizing sustainability tools..

Learn how to to create and use document processors using the Document AI API.

在本实验中，您将学习如何训练自定义 Vertex AI 图片分类模型来识别受损汽车零部件。

Contact Center AI can increase customer satisfaction and operational efficiency by improving call deflection rates, and achieve shorter handling, while making overall operations faster and more effective. In this lab, you'll learn how to use Dialogflow to create a conversational...

Configure an HTTP Load Balancer with global backends, stress test the Load Balancer, and add a Cloud Armor rate limiting policy to restrict based on IP.

在本实验中，您将学习如何使用 Identity-Aware Proxy (IAP) TCP 转发实现对没有外部 IP 地址或不允许通过互联网直接访问的虚拟机实例的管理员权限。

Redacting Sensitive Data with the DLP API

In this lab, you inspect strings and files for sensitive data and then practice using the Cloud Data Loss Prevention (DLP) API to protect data.

In this lab, you will learn how to secure multi-cloud applications using Identity-Aware Proxy (IAP) connector.

In this lab you'll learn how to build a Google Workspace Add-on using Node.js and Cloud Run.

Learn how to use Google AppSheet to enable everyone in your organization to build and extend applications without coding.

In this lab, you use AppSheet to publish your app.

In this lab, you will use AppSheet to implement the UX components in your app.

In this lab, you use AppSheet to connect and configure data for your app.

In this lab, you create a Kubernetes cluster and deploy a simple application to that cluster. Then, you connect the application to the supplied Cloud SQL for PostgreSQL database instance and confirm that it is able to write to and...

In this lab you'll deploy the Autoscaler tool for Cloud Spanner, a companion tool to Cloud Spanner, in the per-project configuration where the autoscaler tools are located in the same project as the Cloud Spanner instance being autoscaled.

In this lab, you explore a Bigtable instance and use the Bigtable CLI (cbt CLI) to query data in Bigtable. You also design a table schema and row key using best practices for Bigtable.

In this lab you’ll learn how to use the Firebase platform to easily create Web applications and implement and deploy a chat client.

In this lab, you will configure a highly available Memorystore cluster and review the architecture of such a deployment.

Learn to migrate MySQL Databases to BigQuery using Datastream and Dataflow

Learn how to analyze key parts of a tennis serve using the Video Intelligence API. To preview, watch the short video this lab is based off of.

In this lab, you create and run a Sensitive Data Protection job using the De-identify (DeID) Findings Action to create a redacted and de-identified copy data in Cloud Storage.

In this lab, you will implement a player profile service and a game matchmaking service to work with Cloud Spanner.

In this lab, you will run BigQuery analytics on data stored in AWS S3 using BigQuery Omni.

In this lab, you create Cloud Run functions that respond to HTTP calls and are triggered by Pub/Sub messages and Cloud Audit Logs.