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Arun-Somu Panneerselvam

成为会员时间:2021

钻石联赛

34380 积分
Gen AI: Navigate the Landscape Earned Aug 29, 2025 EDT
Gen AI: Unlock Foundational Concepts Earned Aug 28, 2025 EDT
Gen AI: Beyond the Chatbot Earned Aug 23, 2025 EDT
Build intelligent agents with Agent Development Kit (ADK) Earned Aug 23, 2025 EDT
Serverless Data Processing with Dataflow: Foundations Earned Dec 13, 2024 EST
Professional Machine Learning Engineer Study Guide Earned Nov 5, 2024 EST
Document AI Earned Jun 15, 2024 EDT
Generative AI for Document Processing Earned Jun 3, 2024 EDT
Build Custom Processors with Document AI Earned Jun 2, 2024 EDT
Custom Search with Embeddings in Vertex AI Earned May 30, 2024 EDT
App Dev with Gemini Earned May 11, 2024 EDT
Improving developer velocity with Gemini Code Assist Earned May 11, 2024 EDT
Text Prompt Engineering Techniques Earned Apr 14, 2024 EDT
Vertex AI Studio 簡介 Earned Apr 13, 2024 EDT
Getting Started with the Vertex AI Gemini API Earned Apr 11, 2024 EDT
Vector Search 和嵌入 Earned Mar 17, 2024 EDT
Search with AI Applications Earned Mar 16, 2024 EDT
Document AI: Building a Custom Document Extractor Earned Mar 3, 2024 EST
Text Prompt Engineering Techniques Earned Mar 3, 2024 EST
Generative AI for Business Leaders Earned Mar 1, 2024 EST
生成式 AI 簡介 Earned Feb 25, 2024 EST
Generative AI Explorer : Vertex AI Earned Feb 17, 2024 EST
Building Gen AI Apps with Vertex AI: Prompting and Tuning Earned Feb 17, 2024 EST
Generative AI Fundamentals Earned Feb 9, 2024 EST
建立圖像說明生成模型 Earned Jan 31, 2024 EST
Transformer 和 BERT 模型 Earned Jan 31, 2024 EST
Vertex AI Studio 簡介 Earned Jan 31, 2024 EST
編碼器-解碼器架構 Earned Jan 31, 2024 EST
注意力機制 Earned Jan 30, 2024 EST
圖像生成簡介 Earned Jan 9, 2024 EST
負責任的 AI 技術:透過 Google Cloud 採用 AI 開發原則 Earned Jan 9, 2024 EST
Generative AI Fundamentals - 繁體中文 Earned Jan 4, 2024 EST
負責任的 AI 技術簡介 Earned Jan 3, 2024 EST
大型語言模型簡介 Earned Oct 28, 2023 EDT
Cloud Run Skills Earned Sep 16, 2023 EDT
生成式 AI 簡介 Earned Aug 9, 2023 EDT
Google Cloud Essentials Earned Mar 31, 2023 EDT
開始使用 Google Kubernetes Engine Earned Feb 7, 2023 EST
可靠的 Google Cloud 基礎架構:設計與程序 Earned Jan 16, 2023 EST
重要的 Google Cloud 基礎架構:核心服務 Earned Nov 19, 2022 EST
重要的 Google Cloud 基礎架構:基本概念 Earned Nov 1, 2022 EDT
Google Cloud 基礎知識:核心基礎架構 Earned Oct 25, 2022 EDT
Preparing for your Professional Cloud Architect Journey Earned Oct 3, 2022 EDT

Gen AI: Navigate the Landscape is the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.

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Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.

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Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.

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This structured course is for developers interested in building intelligent agents using the Agent Development Kit (ADK). It combines hands-on experience, core concepts, and practical application, to provide a comprehensive guide to using ADK. You can also join our community of Google Cloud experts and peers to ask questions, collaborate on answers, and connect with the Googlers making the products you use every day.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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This course helps learners create a study plan for the PMLE (Professional Machine Learning 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 provides partners the skills required to scope, design and deploy Document AI solutions for enterprise customers utilizing use-cases from both the procurement and lending arenas.

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Explore how to use AI to automate document processing tasks, such as classifying documents, extracting data from documents, and summarizing documents. Learn how to use the Document AI Workbench to create custom document extractors and summarizers. Upload documents, define fields, create versions, and call endpoints to get structured data and summaries back. Discover a new service called Document AI Warehouse, which is a fully managed service to search, store, govern, and manage documents and their extracted metadata. You will also learn about how it integrates with other Google Cloud services like Document AI, BigQuery, and Cloud Storage.

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This skill badge course is designed to offer hands-on experience through labs, enabling participants to master Document AI for document processing and extraction tasks. By the end of the course, participants will be proficient in creating and testing Document AI processors, customizing document extraction using Document AI Workbench, and building custom processors to tackle real-world document processing challenges.

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

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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.

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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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本課程說明如何使用深度學習來建立圖像說明生成模型。您將學習圖像說明生成模型的各個不同組成部分,例如編碼器和解碼器,以及如何訓練和評估模型。在本課程結束時,您將能建立自己的圖像說明生成模型,並使用模型產生圖像說明文字。

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這堂課程將說明變換器架構,以及基於變換器的雙向編碼器表示技術 (BERT) 模型,同時帶您瞭解變換器架構的主要組成 (如自我注意力機制) 和如何用架構建立 BERT 模型。此外,也會介紹 BERT 適用的各種任務,像是文字分類、問題回答和自然語言推論。課程預計約 45 分鐘。

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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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本課程概要說明解碼器與編碼器的架構,這種強大且常見的機器學習架構適用於序列對序列的任務,例如機器翻譯、文字摘要和回答問題。您將認識編碼器與解碼器架構的主要元件,並瞭解如何訓練及提供這些模型。在對應的研究室逐步操作說明中,您將學習如何從頭開始使用 TensorFlow 寫程式,導入簡單的編碼器與解碼器架構來產生詩詞。

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本課程將介紹注意力機制,說明這項強大技術如何讓類神經網路專注於輸入序列的特定部分。此外,也將解釋注意力的運作方式,以及如何使用注意力來提高各種機器學習任務的成效,包括機器翻譯、文字摘要和回答問題。

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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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完成「Introduction to Generative AI」、「Introduction to Large Language Models」和「Introduction to Responsible AI」課程,即可獲得技能徽章。通過最終測驗,就能展現您對生成式 AI 基本概念的掌握程度。 「技能徽章」是 Google Cloud 核發的數位徽章,用於表彰您對 Google Cloud 產品和服務的相關知識。您可以將技能徽章公布在社群媒體的個人資料中,向其他人分享您的成果。

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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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Welcome Gamers! Learn Cloud Run Skills, all while having fun! In this game you will build a PDF converter web app on Cloud Run, learn to Use Cloud Run and Pub/Sub, build a REST API with Go and Cloud Run, and finally develop an application to create PDFs on Google Cloud. Earn the points by completing the steps in the lab... Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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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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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

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歡迎參加「開始使用 Google Kubernetes Engine」課程。Kubernetes 是位於應用程式和硬體基礎架構之間的軟體層。如果您對這項技術感興趣,這堂課程可以滿足您的需求。有了 Google Kubernetes Engine,您就能在 Google Cloud 中以代管服務的形式使用 Kubernetes。 本課程的目標在於介紹 Google Kubernetes Engine (常簡稱為 GKE) 的基本概念,以及如何將應用程式容器化,以便在 Google Cloud 中執行。課程首先會初步介紹 Google Cloud,隨後簡介容器、Kubernetes、Kubernetes 架構和 Kubernetes 作業。

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這堂課程可讓參加人員瞭解如何使用確實有效的設計模式,在 Google Cloud 中打造相當可靠且效率卓越的解決方案。這堂課程接續了「設定 Google Compute Engine 架構」或「設定 Google Kubernetes Engine 架構」課程的內容,並假設參加人員曾實際運用上述任一課程涵蓋的技術。這堂課程結合了簡報、設計活動和實作研究室,可讓參加人員瞭解如何定義業務和技術需求,並在兩者之間取得平衡,設計出相當可靠、可用性高、安全又符合成本效益的 Google Cloud 部署項目。

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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務,並將重點放在 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室,可讓參加人員探索及部署解決方案元素,例如網路、系統和應用程式服務等基礎架構元件。另外,這堂課也會介紹如何部署實用的解決方案,包括客戶提供的加密金鑰、安全性和存取權管理機制、配額與帳單,以及資源監控功能。

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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務,尤其側重於 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室,可讓參加人員探索及部署解決方案元素,例如網路、虛擬機器和應用程式服務等基礎架構元件。您會瞭解如何透過控制台和 Cloud Shell 使用 Google Cloud。另外,您也能瞭解雲端架構師的職責、基礎架構設計方法,以及具備虛擬私有雲 (VPC)、專案、網路、子網路、IP 位址、路徑和防火牆規則的虛擬網路設定。

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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 helps learners create a study plan for the PCA (Professional Cloud Architect) 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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