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.

說明如何使用 Go 和 Cloud Run 建構 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.

在本實驗室中，您將於 Cloud Run 建構無伺服器的 PDF 轉換器網頁應用程式，並能利用這個應用程式，自動將 Cloud Storage 中的檔案轉換為 PDF，儲存至個別資料夾。

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.

在本實驗室中，您將學習如何實作 BigLake 資料表。

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.

在本實驗室中，我們將講解如何利用 BigQuery ML XGBoost 模型進行訓練、調整、評估及解讀，並產生批次及線上預測結果。您將利用實際行動應用程式「Flood it!」中的 Google Analytics 4 資料集，判定使用者回訪應用程式的機率。此外，您也會運用 BigQuery ML 模型產生批次預測結果，並將該模型匯出並部署至 Vertex AI，以利線上預測。

在這個研究室中，您會訓練 TensorFlow 模型並部署至 Vertex AI，藉此執行預測作業。建議您觀看下列短片：《Getting started with Notebooks for machine learning》和《Faster model training and experimentation with Vertex AI》。

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

Cloud Natural Language API 可讓您從一段文字中擷取實體，並執行情緒和語法分析。在這個實作研究室中，您將學習如何使用 Cloud Natural Language API，從文字內容中擷取實體和情緒。

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.

在本實驗室中，您將瞭解如何訓練自訂 Vertex AI 圖像分類模型，以識別受損的汽車零件。

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.

在本研究室中，您將瞭解如何使用 Identity-Aware Proxy (IAP) TCP 轉送功能，為沒有外部 IP 位址，或不允許直接透過網際網路存取的 VM 執行個體，啟用管理員權限。

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.