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Olona Xhakana

成为会员时间：2024

钻石联赛

85496 积分
Google Cloud 基礎知識：核心基礎架構 Earned Sep 18, 2025 EDT
Exploring Data Transformation with Google Cloud Earned Jun 26, 2025 EDT
Digital Transformation with Google Cloud Earned Jun 26, 2025 EDT
開始使用 Pub/Sub Earned Jun 9, 2025 EDT
Understanding LookML in Looker Earned Jun 6, 2025 EDT
透過 Vertex AI 建構及部署機器學習解決方案 Earned May 29, 2025 EDT
運用 BigQuery ML 建立機器學習模型 Earned May 29, 2025 EDT
開發人員的負責任 AI 技術：公平性與偏誤 Earned May 28, 2025 EDT
開發人員的負責任 AI 技術：隱私權與安全性 Earned May 28, 2025 EDT
在 Google Cloud 打造生成式 AI 應用程式 Earned May 28, 2025 EDT
開發人員的負責任 AI 技術：可解釋性與透明度 Earned May 27, 2025 EDT
Working with Notebooks in Vertex AI Earned May 21, 2025 EDT
機器學習運作 (MLOps) 與 Vertex AI：模型評估 Earned May 14, 2025 EDT
大型語言模型簡介 Earned May 13, 2025 EDT
生成式 AI 適用的機器學習運作 (MLOps) Earned May 13, 2025 EDT
生成式 AI 簡介 Earned May 13, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned May 13, 2025 EDT
Machine Learning Operations (MLOps): Getting Started Earned May 13, 2025 EDT
為 Looker 資訊主頁和報表準備資料 Earned Apr 23, 2025 EDT
Feature Engineering Earned Mar 25, 2025 EDT
Machine Learning in the Enterprise Earned Mar 25, 2025 EDT
Production Machine Learning Systems Earned Mar 21, 2025 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Mar 20, 2025 EDT
Launching into Machine Learning Earned Mar 13, 2025 EDT
Google Cloud 的 AI 和機器學習服務簡介 Earned Mar 11, 2025 EDT
透過 Gemini in BigQuery 提升工作效率 Earned Nov 8, 2024 EST
在 BigQuery 使用 Gemini 模型 Earned Nov 8, 2024 EST
Google Cloud 中的資料工程簡介 Earned Nov 8, 2024 EST
Serverless Data Processing with Dataflow: Develop Pipelines Earned Nov 7, 2024 EST
使用 Dataplex 建構資料網格 Earned Nov 4, 2024 EST
使用 BigQuery ML 為預測模型進行資料工程 Earned Nov 4, 2024 EST
透過 BigQuery 建構資料倉儲 Earned Oct 11, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned Oct 7, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Sep 17, 2024 EDT
Build Streaming Data Pipelines on Google Cloud Earned Sep 16, 2024 EDT
Build Batch Data Pipelines on Google Cloud Earned Sep 13, 2024 EDT
Serverless Data Processing with Dataflow: Operations Earned Sep 10, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned Sep 9, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 5, 2024 EDT
Preparing for your Professional Data Engineer Journey Earned Aug 27, 2024 EDT

「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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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences. “Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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There's much excitement about cloud technology and digital transformation, but often many unanswered questions. For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin? If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.

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完成「開始使用 Pub/Sub」任務，即可獲得 技能徽章。 技能徽章課程，您將瞭解如何透過 Cloud 控制台使用 Pub/Sub、 如何使用 Cloud Scheduler 工作節省心力，並透過 Pub/Sub Lite 節省擷取大量事件 的費用。

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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.

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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「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。 「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熟技能， 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實驗室，即可取得技能徽章 並與他人分享。

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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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本課程涵蓋「AI 隱私權」和「AI 安全性」這兩個重要主題。我們將介紹實用的方法和工具，協助您運用 Google Cloud 產品和開放原始碼工具，導入 AI 隱私權和安全性的建議做法。

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大型語言模型 (LLM) 誕生之後，生成式 AI 應用程式帶來的嶄新使用者體驗，可說是幾乎前所未有。身為應用程式開發人員，您要如何在 Google Cloud，運用生成式 AI 建立出色的互動式應用程式？ 本課程將帶您瞭解生成式 AI 應用程式，以及如何使用提示設計和檢索增強生成 (RAG)，透過 LLM 建構強大的應用程式。我們也會介紹可用於正式環境的生成式 AI 應用程式架構。您將建構採用 LLM 和 RAG 的對話應用程式。

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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 is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

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本課程針對評估生成式和預測式 AI 模型，向機器學習從業人員介紹相關的基礎工具、技術和最佳做法。模型評估是機器學習的重要領域，確保這類系統能在正式環境中提供可靠、準確且成效優異的結果。 學員將深入瞭解多種評估指標與方法，以及適用於不同模型類型和工作的應用方式。此外，也會特別介紹生成式 AI 模型帶來的獨特難題，並提供有效的應對策略。透過 Google Cloud Vertex AI 平台，學員將瞭解在模型挑選、最佳化和持續監控方面，該如何導入穩健的評估程序。

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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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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。

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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 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 Vertex AI 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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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 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 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 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 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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本課程介紹 Google Cloud 中的 AI 和機器學習 (ML) 服務。這些服務可建構預測式和生成式 AI 專案。我們將帶您探索「從資料到 AI」生命週期中適用的技術、產品和工具，包括 AI 基礎、開發選項及解決方案。課程目的是藉由生動的學習體驗與實作練習，增進數據資料學家、AI 開發人員和機器學習工程師的技能與知識。

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本課程會說明 Gemini in BigQuery，這是一套由 AI 輔助的功能，可協助「從資料到 AI」的工作流程。這些功能包含資料探索和準備、程式碼生成和疑難排解，以及工作流程探索和視覺化。本課程將透過概念解說、應用實例和實作實驗室，協助資料從業人員提升工作效率，並加速開發 pipeline。

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本課程將示範如何在 BigQuery 運用 AI/機器學行模型，以執行生成式 AI 任務。透過涉及顧客關係管理的應用實例，您將瞭解運用 Gemini 模型解決業務問題的工作流程。為了便於理解，本課程還提供了採用 SQL 查詢和 Python 筆記本的程式設計解決方案，指導您逐步操作。

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在本課程中，您會學到 Google Cloud 上的資料工程、資料工程師的角色與職責，以及這些內容如何對應至 Google Cloud 提供的服務。您也將瞭解處理資料工程難題的許多方法。

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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完成「使用 Dataplex 建構資料網格」技能徽章入門課程，即可證明您具備下列技能：使用 Dataplex 建構資料網格， 以利在 Google Cloud 維護資料安全性，並協助治理和探索資料。您將練習並測試自己的技能，包括在 Dataplex 為資產加上標記、指派 IAM 角色，以及評估資料品質。

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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。

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完成 透過 BigQuery 建構資料倉儲 技能徽章中階課程，即可證明您具備下列技能： 彙整資料以建立新資料表、排解彙整作業問題、利用聯集附加資料、建立依日期分區的資料表， 以及在 BigQuery 使用 JSON、陣列和結構體。 「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中應用相關 知識。完成技能徽章課程及結業評量挑戰研究室， 即可取得技能徽章並與他人分享。

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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 Dataproc 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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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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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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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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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