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Zafar Abdugaffarov

Member since 2023

Diamond League

56720 points
Cloud 運算中的安全性原則簡介 Earned Dec 16, 2025 EST
Cloud 安全風險管理策略 Earned Dec 16, 2025 EST
Cloud 安全風險：找出並防範威脅 Earned Dec 16, 2025 EST
運用 Google Cloud 偵測、因應網路攻擊並執行復原工作 Earned Dec 16, 2025 EST
全面整合：為成為 Cloud 資安分析師做好準備 Earned Dec 16, 2025 EST
Put It All Together: Prepare for a Cloud Security Analyst Job Earned Jul 16, 2024 EDT
Detect, Respond, and Recover from Cloud Cybersecurity Attacks Earned Jul 15, 2024 EDT
Cloud Security Risks: Identify and Protect Against Threats Earned Jun 13, 2024 EDT
Strategies for Cloud Security Risk Management Earned May 29, 2024 EDT
Introduction to Security Principles in Cloud Computing Earned May 16, 2024 EDT
Google Cloud 基礎知識：核心基礎架構 Earned Apr 23, 2024 EDT
Build a Certification Study Guide: ACE Exam Prep Earned Apr 13, 2024 EDT
探索生成式 AI - Agent Platform Earned Apr 7, 2024 EDT
Vertex AI Studio 簡介 Earned Apr 7, 2024 EDT
建立圖像說明生成模型 Earned Apr 2, 2024 EDT
Transformer 和 BERT 模型 Earned Apr 2, 2024 EDT
編碼器-解碼器架構 Earned Apr 1, 2024 EDT
注意力機制 Earned Mar 31, 2024 EDT
圖像生成簡介 Earned Mar 31, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則 Earned Mar 20, 2024 EDT
在 Agent Platform 設計提示 Earned Mar 19, 2024 EDT
負責任的 AI 技術簡介 Earned Mar 12, 2024 EDT
大型語言模型簡介 Earned Mar 12, 2024 EDT
生成式 AI 簡介 Earned Mar 12, 2024 EDT
Looker 資料模型管理 Earned Mar 9, 2024 EST
在 Looker 建構 LookML 物件 Earned Mar 6, 2024 EST
Data Catalog Fundamentals Earned Feb 28, 2024 EST
在 Looker 應用進階 LookML 概念 Earned Feb 28, 2024 EST
運用 BigQuery ML 建立機器學習模型 Earned Feb 22, 2024 EST
從 BigQuery 資料取得深入分析結果 Earned Feb 21, 2024 EST
為 Looker 資訊主頁和報表準備資料 Earned Feb 20, 2024 EST
Developing Data Models with LookML Earned Feb 20, 2024 EST
Analyzing and Visualizing Data in Looker Earned Feb 4, 2024 EST
Google Cloud 資料分析基本概念 Earned Jan 25, 2024 EST
透過 Vertex AI 建構及部署機器學習解決方案 Earned Nov 16, 2023 EST
在 Google Cloud 為機器學習 API 準備資料 Earned Nov 5, 2023 EST
ML Pipelines on Google Cloud Earned Oct 31, 2023 EDT
MLOps with Agent Platform: Manage Features Earned Oct 7, 2023 EDT
Machine Learning Operations (MLOps): Getting Started Earned Oct 5, 2023 EDT
Recommendation Systems on Google Cloud Earned Oct 5, 2023 EDT
Google Cloud 的 AI 和機器學習服務簡介 Earned Sep 28, 2023 EDT
Natural Language Processing on Google Cloud Earned Sep 21, 2023 EDT
Computer Vision Fundamentals with Google Cloud Earned Sep 5, 2023 EDT
Production Machine Learning Systems Earned Aug 23, 2023 EDT
Machine Learning in the Enterprise Earned Aug 21, 2023 EDT
[DEPRECATED] Feature Engineering Earned Jul 27, 2023 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned Jul 9, 2023 EDT
Launching into Machine Learning Earned Jun 26, 2023 EDT
How Google Does Machine Learning Earned Jun 8, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned May 24, 2023 EDT

這是 Google Cloud 網路安全專業證書五堂課程中的第一堂。本課程將介紹網路安全的必要基礎，包括安全防護生命週期、數位轉型和雲端運算的重要概念。您將瞭解初級雲端資安分析師用來自動執行工作的常見工具。

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這是 Google Cloud 網路安全專業證書五堂課程中的第二堂。本課程將介紹廣泛使用的雲端風險管理架構，探討安全領域、法規遵循生命週期，以及 HIPAA、NIST CSF 和 SOC 等業界標準。您將學會識別風險、實作安全控管措施、評估法規遵循情形，以及管理資料保護作業。此外，您還將實際操作 Google Cloud 和多雲端工具，瞭解如何因應風險和法規遵循需求。本課程也納入求職和面試準備技巧，提供全方位基礎知識，協助學員瞭解並有效應對雲端風險管理的複雜現況。

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這是 Google Cloud 網路安全專業證書五堂課程中的第三堂。本課程將探討雲端環境中的身分管理和存取控管原則，涵蓋 AAA (驗證、授權和稽核)、憑證處理和憑證管理等重要元素。此外，您還會學到威脅與安全漏洞管理、雲端原生原則和資料保護措施等重要主題。您完成本課程後，將具備保護雲端資源和組織機密資訊所需的技能與知識。此外，您還能繼續使用跟職涯有關的資源，精進面試技巧，準備邁向職涯發展的下一步。

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這是 Google Cloud 網路安全專業證書五堂課程中的第四堂。本課程將著重於培養記錄、安全防護和快訊監控能力，以及攻擊防範技術。您將學到如何自訂威脅資訊、管理事件、處理危機溝通、進行根本原因分析，以及掌握事件應變和事後溝通的訣竅。您將學習如何使用 Google Cloud 工具找出入侵指標，並為業務永續性和災難復原做好準備。除了技術能力，您還需要持續更新履歷，並練習面試技巧。

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這是 Google Cloud 網路安全專業證書課程的第五門課，在本課程中，您將在互動式總結專案中，結合並應用雲端安全原則、風險管理、安全漏洞識別、事件管理和危機溝通等重要概念。此外，您還會完成履歷更新，並實際練習所有新學到的面試技巧，為應徵和面試這個領域的工作做好準備。

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This is the fifth of five courses in the Google Cloud Cybersecurity Certificate. In this course, you’ll combine and apply key concepts such as cloud security principles, risk management, identifying vulnerabilities, incident management, and crisis communications in an interactive capstone project. Additionally, you'll finalize your resume updates and put to practice all the new interview techniques you've learned, preparing you to confidently apply for and interview for jobs in the field.

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This is the fourth of five courses in the Google Cloud Cybersecurity Certificate. In this course, you’ll focus on developing capabilities in logging, security, and alert monitoring, along with techniques for mitigating attacks. You'll gain valuable knowledge in customizing threat feeds, managing incidents, handling crisis communications, conducting root cause analysis, and mastering incident response and post-event communications. Using Google Cloud tools, you'll learn to identify indicators of compromise and prepare for business continuity and disaster recovery. Alongside these technical skills, you'll continue updating your resume and practicing interview techniques.

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This is the third of five courses in the Google Cloud Cybersecurity Certificate. In this course, you’ll explore the principles of identity management and access control within a cloud environment, covering key elements like AAA (Authentication, Authorization, and Auditing), credential handling, and certificate management. You'll also explore essential topics in threat and vulnerability management, cloud-native principles, and data protection measures. Upon completing this course, you will have acquired the skills and knowledge necessary to secure cloud-based resources and safeguard sensitive organizational information. Additionally, you'll continue to engage with career resources and hone your interview techniques, preparing you for the next step in your professional journey.

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This is the second of five courses in the Google Cloud Cybersecurity Certificate. In this course, you’ll explore widely-used cloud risk management frameworks, exploring security domains, compliance lifecycles, and industry standards such as HIPAA, NIST CSF, and SOC. You'll develop skills in risk identification, implementation of security controls, compliance evaluation, and data protection management. Additionally, you'll gain hands-on experience with Google Cloud and multi-cloud tools specific to risk and compliance. This course also incorporates job application and interview preparation techniques, offering a comprehensive foundation to understand and effectively navigate the complex landscape of cloud risk management.

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This is the first of five courses in the Google Cloud Cybersecurity Certificate. In this course, you’ll explore the essentials of cybersecurity, including the security lifecycle, digital transformation, and key cloud computing concepts. You’ll identify common tools used by entry-level cloud security analysts to automate tasks.

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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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Learn how to use Gemini Notebook to create a personalized study guide for the Associate Cloud Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook , and learn how to use a study guide to practice for a certification exam.

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「探索生成式 AI - Agent Platform」課程包含一系列實驗室， 介紹如何在 Google Cloud 使用生成式 AI。完成這些實驗室之後，您會瞭解如何 在 Agent Platform 使用 Gemini 模型、提示詞設計技巧與最佳做法，以及 AI 在構思想法和文字分類、擷取、摘要等方面的用途。您也會瞭解如何運用 Agent Platform 的自訂訓練功能調整基礎模型， 並部署至 Agent Platform 中的端點。

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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 在 Agent Platform 設計提示 技能徽章入門課程，即可證明您具備下列技能： 在 Agent Platform 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。

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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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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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完成 Looker 資料模型管理技能徽章中階課程，即可證明您具備下列技能：維護 LookML 專案的健全性、運用 SQL Runner 驗證資料、採用 LookML 最佳做法、改良查詢及 報表，並執行永久衍生資料表和快取政策。

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完成「在 Looker 建構 LookML 物件」技能徽章入門課程， 即可證明您具備下列技能： 建立新的維度和測量指標、檢視畫面和衍生資料表；根據需求設定測量指標篩選器和類型； 更新維度和測量指標； 建構及調整「探索」；將檢視表彙整至現有「探索」；以及配合業務需求決定要建立哪些 LookML 物件。

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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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在本課程中，您將透過實際操作，瞭解如何在 Looker 應用進階 LookerML 概念。您將學習如何使用 Liquid 自訂和建立動態維度 和測量指標、建構動態 SQL 衍生資料表和自訂的原生衍生資料表， 並運用擴充參數將 LookML 程式碼模組化。

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完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程，即可證明您具備下列技能： 可使用 BigQuery ML 建立及評估機器學習模型，並根據資料進行預測。

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完成 從 BigQuery 資料取得深入分析結果 技能徽章入門課程，即可證明您具備下列技能： 撰寫 SQL 查詢、查詢公開資料表、將樣本資料載入 BigQuery、使用 BigQuery 的查詢驗證工具 排解常見語法錯誤，以及在 Data Studio 中 透過連結 BigQuery 資料建立報表。

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完成「為 Looker 資訊主頁和報表準備資料」技能徽章入門課程， 即可證明您具備下列技能：可篩選、排序和 pivot 資料、合併不同的 Looker 探索結果， 還能使用函式和運算子建構 Looker 資訊主頁和報表，取得資料分析結果和圖表。

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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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瞭解如何在 Google Cloud 將原始數據轉化為明確的資料導向決策。本初級課程提供實作研究室和實際情境，引導您在 BigQuery 查詢資料、在 Looker 探索趨勢，以及透過數據分析工具製作可共用的互動式報表。

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完成 透過 Vertex AI 建構及部署機器學習解決方案 課程，即可瞭解如何使用 Google Cloud 的 Vertex AI 平台、AutoML 和自訂訓練服務， 訓練、評估、調整、解釋及部署機器學習模型。 這個技能徽章課程適合專業數據資料學家和機器學習 工程師，完成即可取得中階技能徽章。技能 徽章是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和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應用相關知識。完成這個技能徽章課程 和結業評量挑戰實驗室，就能獲得數位徽章， 並與親友分享。

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完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Managed Service for Apache Spark 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners will get hands-on practice using Agent Platform Feature Store's streaming ingestion at the SDK layer.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

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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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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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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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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

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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 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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