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Julian Patiño

成为会员时间：2019

青铜联赛

20395 积分
Gemini Enterprise 簡介 Earned Oct 15, 2025 EDT
Developing Applications with Google Cloud: Foundations Earned Oct 6, 2025 EDT
Google Cloud 基礎知識：核心基礎架構 Earned Mar 10, 2025 EDT
Introduction to Gemini Enterprise for Customer Experience Earned Feb 25, 2025 EST
Stateful Flows Earned Sep 19, 2024 EDT
Search with AI Applications Earned Jun 25, 2024 EDT
Text Prompt Engineering Techniques Earned May 29, 2024 EDT
Vertex AI Studio 簡介 Earned Apr 30, 2024 EDT
Generative AI Explorer : Vertex AI Earned Apr 17, 2024 EDT
圖像生成簡介 Earned Feb 23, 2024 EST
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則 Earned Feb 20, 2024 EST
Generative AI Fundamentals Earned Jan 26, 2024 EST
負責任的 AI 技術簡介 Earned Jan 23, 2024 EST
大型語言模型簡介 Earned Jan 5, 2024 EST
Generative AI for Business Leaders Earned Nov 28, 2023 EST
生成式 AI 簡介 Earned Nov 15, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 19, 2023 EDT
建立 Google Cloud 網路 Earned Jul 21, 2023 EDT
Preparing for your Professional Data Engineer Journey Earned Jun 21, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Jun 19, 2023 EDT
Data Warehousing for Partners: BigQuery Extended Capabilities Earned Jun 13, 2023 EDT
Data Warehousing for Partners: Streaming Analytics Earned Jun 13, 2023 EDT
Data Warehousing for Partners: Analyze Data with Looker Earned Jun 12, 2023 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub Earned Jun 11, 2023 EDT
Data Warehousing for Partners: Process Data with Dataflow Earned Jun 9, 2023 EDT
Data Warehousing for Partners: Cloud Data Fusion Pipelines Earned Jun 9, 2023 EDT
Data Warehousing for Partners: Process Data with Dataproc Earned Jun 9, 2023 EDT
Data Warehousing for Partners: Migrate Data to BigQuery Earned Jun 9, 2023 EDT
Data Warehousing for Partners: Optimize in BigQuery Earned Jun 7, 2023 EDT
Data Warehousing for Partners: Design in BigQuery Earned Jun 7, 2023 EDT
Data Warehousing for Partners: Enable Google Cloud Customers Earned Jun 6, 2023 EDT
Google Cloud 必備知識 Earned Oct 7, 2022 EDT
在 Compute Engine 導入 Cloud Load Balancing Earned Apr 20, 2021 EDT
Google Cloud Product Fundamentals Earned Feb 19, 2021 EST
Intro to BigQuery: Analytics & Machine Learning Earned Feb 19, 2021 EST

本課程介紹 Gemini Enterprise，這個強大的平台結合 AI 代理、企業搜尋工具、NotebookLM 和智慧資料存取功能，可協助組織解決難題。學員將能透過實際案例和練習，瞭解 Gemini Enterprise 功能如何滿足實際業務需求、描述平台架構，並說明如何根據不同職務處理資料存取與隱私權事宜。

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In this course, you learn the fundamentals of application development on Google Cloud. You learn best practices for cloud applications, and how to select compute and data options to match your application use cases. You're introduced to generative AI and how it's used to help build applications. You learn about authentication and authorization, application deployment, continuous integration and delivery, and monitoring and performance tuning for your applications running in Google Cloud. Using lectures and hands-on labs, you learn how to get started building and running applications on Google Cloud.

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「Google Cloud 基礎知識：核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室，介紹並比較 Google Cloud 的多種運算和儲存服務，同時提供重要的資源和政策管理工具。

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This course explores the different products and capabilities of Gemini Enterprise for Customer Experience, including CX Agent Studio, Agent Assist and CX Insights. 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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Discover flows in Conversational Agents and learn how to build deterministic chat and voice experiences with language models. Explore key concepts like drivers, intents, and entities, and how to use them to create conversational agents.

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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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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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This content is deprecated. Please see the latest version of the course, here.

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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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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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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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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。

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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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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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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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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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完成 建立 Google Cloud 網路 課程即可獲得技能徽章。這個課程將說明 部署及監控應用程式的多種方法，包括查看 IAM 角色及新增/移除 專案存取權、建立虛擬私有雲網路、部署及監控 Compute Engine VM、編寫 SQL 查詢、在 Compute Engine 部署及監控 VM，以及 使用 Kubernetes 透過多種方法部署應用程式。

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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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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This course explores the Geographic Information Systems (GIS), GIS Visualization, and machine learning enhancements to BigQuery.

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This course explores how to implement a streaming analytics solution using Dataflow and BigQuery.

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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.

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This course explores how to implement a streaming analytics solution using Pub/Sub.

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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataflow.

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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.

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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.

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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.

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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.

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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.

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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.

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在這堂入門課程，您將實際練習使用 Google Cloud 的基礎工具和服務。本課程包含可選擇觀賞的影片， 針對實驗室涵蓋的概念提供更多背景資訊，協助您複習。「Google Cloud 必備知識」 是適合 Google Cloud 學員的第一堂課， 即使您尚未學習或不熟悉雲端知識， 也能從這堂課獲得實務經驗，並應用於第一項 Google Cloud 專案。不管是撰寫 Cloud Shell 指令 和部署第一部虛擬機器，還是在 Kubernetes Engine 或透過負載平衡執行應用程式， 「Google Cloud 必備知識」都是認識平台基本功能的最佳入門資源。

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完成「在 Compute Engine 導入 Cloud Load Balancing」技能徽章入門課程，即可證明您具備下列技能： 在 Compute Engine 建立及部署虛擬機器， 以及設定網路和應用程式負載平衡器。

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This course builds on the Business Transformation with Google Cloud course by taking you on a journey into the technology lens of an organization's transformation. Specifically, we'll explain how an organization can achieve digital transformation using Google Cloud's technology through the following categories: modernizing its IT infrastructure, upgrading the way in which its teams develop applications that run a business, how it can leverage machine learning and artificial intelligence to build new value, how using cloud-based productivity tools like GSuite are essential to the way work gets done, and understand the cost management opportunities and challenges that come with a changing cloud-based IT infrastructure. Throughout the course, we'll explain core concepts such as compute, applications, security, APIs, databases, and their roles in digital transformation. You'll also learn about specific examples where different cloud technology has been used and the benefits they've created…

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Welcome Gamers! Today's game is all about experimenting with Big Query for Machine Learning! Use real life case studies to learn various concepts of BQML and have fun. Take labs to earn points. The faster you complete the lab objectives, the higher your score.

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