Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Apr 15, 2024 EDT
圖像生成簡介
Earned Apr 4, 2024 EDT
Generative AI Explorer : Vertex AI
Earned Apr 4, 2024 EDT
Text Prompt Engineering Techniques
Earned Apr 3, 2024 EDT
Vertex AI Studio 簡介
Earned Apr 3, 2024 EDT
Generative AI for Business Leaders
Earned Apr 2, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Apr 1, 2024 EDT
負責任的 AI 技術簡介
Earned Mar 31, 2024 EDT
大型語言模型簡介
Earned Mar 31, 2024 EDT
生成式 AI 簡介
Earned Mar 31, 2024 EDT
這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。
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這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。
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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助管理員在 Google Cloud 佈建基礎架構。您將瞭解如何透過提示讓 Gemini 解釋基礎架構、部署 GKE 叢集，以及更新既有的基礎架構。在實作研究室中，您也會體驗到 Gemini 如何改良 GKE 的部署工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助開發人員透過 Google Cloud 建構應用程式。您將瞭解如何透過提示讓 Gemini 為您解釋程式碼內容、推薦 Google Cloud 服務，以及生成應用程式的程式碼。在實作研究室中，您也會體驗到 Gemini 如何改良應用程式的開發工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。
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本課程旨在說明 AI 的可解釋性和透明度概念、探討 AI 透明度對開發人員和工程師的重要性。課程中也會介紹實務方法和工具，有助於讓資料和 AI 模型透明且可解釋。
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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.
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Gemini 版 Google Workspace 是一項外掛程式，可讓使用者存取生成式 AI 功能。本課程會使用影片、實作活動和練習範例，深入介紹 Gemini 版 Google Meet 的功能。您會學到如何透過 Gemini 生成背景圖片、提升視訊品質及翻譯字幕。本課程結束後，您將具備 Gemini 版 Google Meet 的知識及技能，安心運用這項工具提高視訊會議的效率。
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安裝 Gemini 版 Google Workspace 外掛程式後，客戶就能在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能，並說明如何在 Google 試算表善用這些功能，提高生產力和效率。
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Gemini 版 Google Workspace 是一項外掛程式，可讓客戶在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能，並說明如何在 Google 簡報善用這些功能，提高生產力和效率。
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使用者將能透過 Gemini 版 Google Workspace 外掛程式運用生成式 AI 功能。本課程會使用影片、實作活動和練習範例，深入介紹 Gemini 版 Google 文件的功能。您將學到如何透過 Gemini 使用提示生成撰寫內容、編輯寫好的文字，以提升整體工作效率。本課程結束後，您將具備 Gemini 版 Google 文件的知識及技能，可自信地運用這項工具提升寫作品質。
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Gemini 版 Google Workspace 是一項外掛程式，可讓客戶在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能，並說明如何在 Gmail 善用這些功能，提高生產力和效率。
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客戶能透過 Gemini 版 Google Workspace 外掛程式在 Google Workspace 使用生成式 AI 功能。本學習路徑會介紹 Gemini 的主要功能，並說明如何在 Google Workspace 善用這些功能，提高生產力和效率。
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本課程旨在說明負責任 AI 技術的概念和 AI 開發原則，同時介紹各項技術，在實務上找出公平性和偏誤，減少 AI/機器學習做法上的偏誤。我們也將探討實用方法和工具，透過 Google Cloud 產品和開放原始碼工具，導入負責任 AI 技術的最佳做法。
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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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本課程旨在提供必要的知識和工具，協助您探索機器學習運作團隊在部署及管理生成式 AI 模型時面臨的獨特挑戰，並瞭解 Vertex AI 如何幫 AI 團隊簡化機器學習運作程序，打造成效非凡的生成式 AI 專案。
本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。
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完成 在 Vertex AI 設計提示 技能徽章入門課程，即可證明您具備下列技能： 在 Vertex AI 設計提示、分析圖片，以及運用多模態模型生成內容。瞭解如何建立有效的提示、引導生成式 AI 輸出內容， 以及將 Gemini 模型用於實際的行銷情境。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰研究室，即可取得技能徽章 並與親友分享。
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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This workload aims to upskill Google Cloud partners to perform specific tasks associated with building a Custom Doc Extractor using the Google Cloud AI solution. The following will be addressed:
Service: Document AI
Task: Extract fields
Processors: Custom Document Extractor and Document Splitter
Prediction: Using Endpoint to programmatically extract fields
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This course explores the different products and capabilities of Customer Engagement Suite (CES) and Conversational agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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Delve into the power of multimodal AI with this project-based course using Gemini. Master essential techniques and build advanced applications. You will:
- Experiment with multimodal use cases to expand application possibilities
- Implement recommendation systems that combine suggestions with clear reasoning
- Design a powerful document search engine using multimodal RAG methods
Important Disclaimer: Please note that these labs are under active development. Functionality may occasionally change or break unexpectedly, and content might be removed or altered without notice. By proceeding with this course, you acknowledge this potential disruption.
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Learn to use LangChain to call Google Cloud LLMs and Generative AI Services and Datastores to simplify complex applications' code.
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Learn how Gemini can revolutionize your ability to develop applications! This course helps developers go beyond the basics and learn how to integrate Gemini into their workflows.
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Unlock the power of Google Cloud's cutting-edge Vertex AI Gemini API to craft innovative multimodal applications. This hands-on course delves into the integration of the Vertex AI SDK for Python, guiding you through the generation of sophisticated responses powered by the Gemini Pro and Gemini Pro Vision models. Get ready to build, deploy, and harness the transformative capabilities of multimodal AI within your own projects.
Important Disclaimer: Please note that these labs are under active development. Functionality may occasionally change or break unexpectedly, and content might be removed or altered without notice. By proceeding with this course, you acknowledge this potential disruption.
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(This course was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.
Get hands-on with the Gemini Pro and Gemini Pro Vision models through our new labs. This course gives you a unique chance to explore these powerful AI tools while our training content is still in development. Learn to interact with the models using the Vertex AI Gemini API and cURL commands, and help us create the best possible learning experience around this technology.
Important Disclaimer: Please note that these labs are under active development. Functionality may occasionally change or break unexpectedly, and content might be removed or altered without notice. By proceeding with this course, you acknowledge this potential disruption.
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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search.
You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.
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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used)
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.
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這堂課程會介紹 AI 搜尋技術、工具和應用程式。主題涵蓋使用向量嵌入執行語意搜尋；結合語意和關鍵字做法的混合型搜尋機制；以及運用檢索增強生成 (RAG) 技術建構有基準的 AI 代理，盡可能減少 AI 幻覺。您可以實際使用 Vertex AI Vector Search，打造智慧型搜尋引擎。
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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.
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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.
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本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。
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This content is deprecated. Please see the latest version of the course, here.
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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。
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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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隨著企業持續擴大使用人工智慧和機器學習，以負責任的方式發展相關技術也日益重要。對許多企業來說，談論負責任的 AI 技術可能不難，如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術，本課程絕對能助您一臂之力。
您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談，協助貴機構奠定良好基礎，實踐負責任的 AI 技術。
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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。
了解详情
這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。
了解详情
這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。
負責任的 AI 技術簡介
Earned Jun 4, 2024 EDT
大型語言模型簡介
Earned Jun 4, 2024 EDT
生成式 AI 簡介
Earned Jun 4, 2024 EDT
使用 Gemini：雲端架構師
Earned Jun 4, 2024 EDT
使用 Gemini：應用程式開發人員
Earned Jun 4, 2024 EDT
Machine Learning in the Enterprise
Earned Jun 4, 2024 EDT
Vertex AI Studio 簡介
Earned Jun 3, 2024 EDT
開發人員的負責任 AI 技術：可解釋性與透明度
Earned May 28, 2024 EDT
Implementing Generative AI with Vertex AI
Earned May 24, 2024 EDT
Gemini in Google Meet
Earned May 17, 2024 EDT
Gemini in Google Sheets
Earned May 17, 2024 EDT
Gemini in Google Slides
Earned May 17, 2024 EDT
Gemini in Google Docs
Earned May 17, 2024 EDT
Gemini in Gmail
Earned May 17, 2024 EDT
Gemini for Google Workspace 簡介
Earned May 17, 2024 EDT
開發人員的負責任 AI 技術：公平性與偏誤
Earned May 16, 2024 EDT
使用 Gemini：端對端 SDLC
Earned May 16, 2024 EDT
生成式 AI 適用的機器學習運作 (MLOps)
Earned May 16, 2024 EDT
建立圖像說明生成模型
Earned May 14, 2024 EDT
Transformer 和 BERT 模型
Earned May 14, 2024 EDT
編碼器-解碼器架構
Earned May 10, 2024 EDT
注意力機制
Earned May 10, 2024 EDT
圖像生成簡介
Earned May 10, 2024 EDT
在 Vertex AI 設計提示
Earned May 9, 2024 EDT
How Google Does Machine Learning
Earned May 7, 2024 EDT
Document AI: Building a Custom Document Extractor
Earned Apr 30, 2024 EDT
Introduction to CES and Conversational Agents
Earned Apr 30, 2024 EDT
Multimodality with Gemini
Earned Apr 30, 2024 EDT
Orchestrate LLM solutions with LangChain
Earned Apr 30, 2024 EDT
Improving developer velocity with Gemini Code Assist
Earned Apr 30, 2024 EDT
App Dev with Gemini
Earned Apr 29, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned Apr 29, 2024 EDT
使用 Gemini 多模態功能和多模態 RAG 檢查複合型文件
Earned Apr 25, 2024 EDT
Getting Started with the Vertex AI Gemini API
Earned Apr 24, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned Apr 24, 2024 EDT
Search with AI Applications
Earned Apr 23, 2024 EDT
Vector Search 和嵌入
Earned Apr 21, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned Apr 18, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Apr 15, 2024 EDT
圖像生成簡介
Earned Apr 4, 2024 EDT
Generative AI Explorer : Vertex AI
Earned Apr 4, 2024 EDT
Text Prompt Engineering Techniques
Earned Apr 3, 2024 EDT
Vertex AI Studio 簡介
Earned Apr 3, 2024 EDT
Generative AI for Business Leaders
Earned Apr 2, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Apr 1, 2024 EDT
負責任的 AI 技術簡介
Earned Mar 31, 2024 EDT
大型語言模型簡介
Earned Mar 31, 2024 EDT
生成式 AI 簡介
Earned Mar 31, 2024 EDT