本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助您透過 Google Cloud 使用 Google 產品和服務，開發、測試、部署及管理應用程式。有了 Gemini 的協助，您會學到如何開發和建構網頁應用程式、修正應用程式中的錯誤、開發測試及查詢資料。在實作研究室中，您也會體驗到 Gemini 如何改良軟體開發生命週期 (SDLC)。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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本課程介紹 Google Cloud 的 AI 和機器學習 (ML) 功能，著重說明如何開發生成式和預測式 AI 專案。我們也會探討「從資料到 AI」整個生命週期都適用的技術、產品和工具，並透過互動式練習，協助資料科學家、AI 開發人員和機器學習工程師精進專業知識。
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
這個簡短的課程會說明如何在 Google Cloud 整合應用程式與 Gemini 1.0 Pro 模型，讓您瞭解 Gemini API 及其生成式 AI 模型，並學習如何透過程式碼存取 Gemini 1.0 Pro 和 Gemini 1.0 Pro Vision 模型。另外，您會在應用程式中使用文字、圖片和影片提示，測試模型的功能。
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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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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.
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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
探索生成式 AI - Vertex AI 課程包含一系列實驗室，幫助您瞭解 如何在 Google Cloud 使用生成式 AI。透過實驗室，您將瞭解 如何使用 Vertex AI PaLM API 系列模型，包括 text-bison、chat-bison、 和 textembedding-gecko。您也會瞭解提示設計、最佳做法、 以及這些模型如何用於構思、文字分類、文字擷取、文字 摘要等。您也會瞭解如何透過 Vertex AI 自訂訓練功能調整基礎模型， 並將模型部署至 Vertex AI 端點。
完成透過 Google Cloud 建構網站技能徽章課程，即可獲得入門級技能徽章。 本課程以 Get Cooking in Cloud 系列影片為基礎， 涵蓋以下主題：在 Cloud Run 部署網站在 Compute Engine 託管網頁應用程式在 Google Kubernetes Engine 建立、 部署及擴充網站使用 Cloud Build 將單體式應用程式遷移至微服務架構
使用 Gemini：端對端 SDLC
Earned Apr 3, 2026 EDT
Applying Machine Learning to your Data with Google Cloud
Earned May 9, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 9, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned May 9, 2024 EDT
Intro to ML: Image Processing
Earned May 8, 2024 EDT
在 Looker 建構 LookML 物件
Earned May 8, 2024 EDT
監控及管理 Google Cloud 資源
Earned May 8, 2024 EDT
使用 Natural Language API 分析情緒
Earned May 8, 2024 EDT
使用 Cloud Vision API 分析圖片
Earned May 8, 2024 EDT
使用 Google API 分析語音和語言
Earned May 8, 2024 EDT
在 Google Cloud 使用 TensorFlow 分類圖像
Earned May 6, 2024 EDT
使用 Gemini：數據資料學家和分析師
Earned May 1, 2024 EDT
機器學習簡介：語言處理
Earned May 1, 2024 EDT
基本概念：基礎架構
Earned May 1, 2024 EDT
使用 Firebase 開發無伺服器應用程式
Earned Apr 26, 2024 EDT
在 Google Cloud 整合應用程式與 Gemini 1.0 Pro
Earned Apr 26, 2024 EDT
Data Catalog Fundamentals
Earned Aug 12, 2023 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Aug 12, 2023 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Aug 12, 2023 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned Aug 12, 2023 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Aug 11, 2023 EDT
使用 Firebase 開發無伺服器應用程式
Earned Aug 11, 2023 EDT
探索生成式 AI - Vertex AI
Earned Aug 11, 2023 EDT
在 Google Cloud 部署 Kubernetes 應用程式
Earned Aug 9, 2023 EDT
運用 BigQuery ML 建立機器學習模型
Earned Aug 9, 2023 EDT
在 Compute Engine 導入 Cloud Load Balancing
Earned Aug 5, 2023 EDT
透過 Google Cloud 建構網站
Earned Aug 2, 2023 EDT