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Jorge Alberto Zúñiga Herrera
Member since 2021
企業 AI 代理與用途
Earned Mar 1, 2026 EST
生成式 AI 代理：實現組織轉型
Earned Dec 11, 2025 EST
綜合應用：為 Cloud 資料分析師工作做準備
Earned Nov 18, 2025 EST
在 Cloud 轉換資料
Earned Nov 18, 2025 EST
在 Cloud 管理與儲存資料
Earned Nov 18, 2025 EST
資料敘事的強大力量：如何在 Cloud 以圖表呈現資料
Earned Nov 18, 2025 EST
Google Cloud 資料分析功能簡介
Earned Nov 18, 2025 EST
透過 Vertex AI 建構及部署機器學習解決方案
Earned Dec 12, 2024 EST
Data Transformation in the Cloud
Earned Aug 29, 2024 EDT
Data Management and Storage in the Cloud
Earned Aug 20, 2024 EDT
使用 Gemini 多模態功能和多模態 RAG 檢查複合型文件
Earned Aug 18, 2024 EDT
Put It All Together: Prepare for a Cloud Data Analyst Job
Earned Aug 13, 2024 EDT
Introduction to Data Analytics in Google Cloud
Earned Aug 9, 2024 EDT
Introduction to Generative AI Studio - 繁體中文
Earned Aug 4, 2024 EDT
Generative AI Fundamentals - 繁體中文
Earned Aug 4, 2024 EDT
在 Agent Platform 設計提示
Earned Aug 4, 2024 EDT
負責任的 AI 技術簡介
Earned Aug 2, 2024 EDT
生成式 AI 簡介
Earned Aug 2, 2024 EDT
大型語言模型簡介
Earned Aug 1, 2024 EDT
The Power of Storytelling: How to Visualize Data in the Cloud
Earned Aug 1, 2024 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Dec 6, 2022 EST
[DEPRECATED] Data Engineering
Earned Dec 5, 2022 EST
Modernize Infrastructure and Applications with Google Cloud
Earned Dec 4, 2022 EST
Understanding LookML in Looker
Earned Dec 2, 2022 EST
Developing a Google SRE Culture
Earned Nov 30, 2022 EST
Deploying SAP on Google Cloud
Earned Nov 29, 2022 EST
Applying Machine Learning to your Data with Google Cloud
Earned Nov 16, 2022 EST
Achieving Advanced Insights with BigQuery
Earned Nov 15, 2022 EST
Exploring and Preparing your Data with BigQuery
Earned Nov 8, 2022 EST
建立及管理 Bigtable 執行個體
Earned Nov 5, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Locales
Earned Nov 4, 2022 EDT
建立及管理 Cloud Spanner 執行個體
Earned Nov 3, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Nov 2, 2022 EDT
Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
Earned Nov 1, 2022 EDT
Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
Earned Oct 31, 2022 EDT
Google Cloud 運算的基本概念：Google Cloud 基礎架構
Earned Oct 30, 2022 EDT
Google Cloud 運算的基本概念：Cloud 運算基礎知識
Earned Oct 29, 2022 EDT
Building Resilient Streaming Analytics Systems on Google Cloud - Locales
Earned Oct 27, 2022 EDT
Recommendation Systems on Google Cloud
Earned Oct 26, 2022 EDT
Machine Learning in the Enterprise
Earned Oct 24, 2022 EDT
Natural Language Processing on Google Cloud
Earned Oct 22, 2022 EDT
Production Machine Learning Systems
Earned Oct 21, 2022 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Aug 6, 2022 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Jul 28, 2022 EDT
Deprecated : Managing Machine Learning Projects with Google Cloud
Earned Apr 11, 2022 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI
Earned Apr 9, 2022 EDT
讓您的 Google Cloud 支出發揮最大效益
Earned Feb 2, 2022 EST
瞭解 Google Cloud 費用
Earned Feb 2, 2022 EST
Machine Learning in the Enterprise - Locales
Earned Feb 1, 2022 EST
Migrating to Google Cloud - Locales
Earned Feb 1, 2022 EST
Achieving Advanced Insights with BigQuery - Locales
Earned Jan 31, 2022 EST
Security Best Practices in Google Cloud
Earned Jan 30, 2022 EST
設定 Google Cloud 網路
Earned Jan 29, 2022 EST
Mitigating Security Vulnerabilities on Google Cloud
Earned Jan 24, 2022 EST
Managing Security in Google Cloud
Earned Jan 18, 2022 EST
Scaling with Google Cloud Operations
Earned Jan 17, 2022 EST
Introduction to Cloud Identity
Earned Jan 16, 2022 EST
Google Cloud Fundamentals for AWS Professionals
Earned Jan 14, 2022 EST
Google Cloud 基礎知識：核心基礎架構
Earned Jan 14, 2022 EST
DEPRECATED Cloud Architecture
Earned Jan 11, 2022 EST
DEPRECATED IoT in the Google Cloud
Earned Jan 10, 2022 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Jan 9, 2022 EST
Scientific Data Processing
Earned Jan 8, 2022 EST
DEPRECATED Language, Speech, Text, & Translation with Google Cloud APIs
Earned Jan 5, 2022 EST
Machine Learning in the Enterprise - Locales
Earned Jan 3, 2022 EST
Feature Engineering - Locales
Earned Jan 2, 2022 EST
TensorFlow on Google Cloud - Locales
Earned Jan 1, 2022 EST
Intro to ML: Image Processing
Earned Dec 31, 2021 EST
機器學習簡介：語言處理
Earned Dec 29, 2021 EST
基本概念：資料、機器學習和 AI
Earned Dec 28, 2021 EST
Data Science on Google Cloud
Earned Dec 26, 2021 EST
Launching into Machine Learning
Earned Dec 25, 2021 EST
How Google Does Machine Learning - Locales
Earned Dec 22, 2021 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Dec 19, 2021 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Dec 16, 2021 EST
使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL
Earned Dec 14, 2021 EST
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned Dec 12, 2021 EST
使用 BigQuery 進行機器學習
Earned Dec 11, 2021 EST
DEPRECATED BigQuery for Marketing Analysts
Earned Dec 9, 2021 EST
Data Catalog Fundamentals
Earned Dec 7, 2021 EST
DEPRECATED BigQuery Basics for Data Analysts
Earned Dec 6, 2021 EST
DEPRECATED BigQuery for Data Warehousing
Earned Dec 5, 2021 EST
透過 BigQuery 建構資料倉儲
Earned Dec 3, 2021 EST
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Dec 2, 2021 EST
基本概念：基礎架構
Earned Nov 30, 2021 EST
安全性與身分識別基礎知識
Earned Nov 29, 2021 EST
Machine Learning APIs
Earned Nov 27, 2021 EST
雲端架構：設計、實作與管理
Earned Nov 23, 2021 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned Nov 21, 2021 EST
雲端工程
Earned Nov 19, 2021 EST
Google Cloud 必備知識
Earned Nov 17, 2021 EST
Cloud SQL
Earned Nov 15, 2021 EST
在 Cloud Data Fusion 建構免程式碼管道
Earned Nov 8, 2021 EST
瞭解 AI 代理如何發揮更高的業務影響力，包括根據您的 KPI 規劃要使用的代理類型，以及探索能解決實際瓶頸的用途。您也將認識各種無程式碼到高程式碼解決方案，瞭解 Gemini Enterprise 如何協助建構和自動調度合適的代理。
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「生成式 AI 代理：實現組織轉型」是 Gen AI Leader 學習路徑的第五堂也是最後一堂課程。本課程將探討組織如何運用自訂生成式 AI 代理，解決特定的業務難題。您將動手練習建構基本的生成式 AI 代理，同時探索這類代理的各種元件，例如模型、推論迴圈和工具。
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這是 Google Cloud Data Analytics 專業證書五堂課程中的第五堂。在本課程中，您將結合第一到第四堂課程的基礎知識和技能，並活用所學，透過專題實作呈現完整的資料生命週期。您將練習使用雲端式工具，有效率地取得、儲存、處理、分析資料，並以圖表呈現及傳達資料洞察結果。本課程結束後，您將完成一項專案，證明自己精通於有效整理多個來源的資料、向不同相關人士說明解決方案，以及運用雲端式軟體將資料洞察結果以圖表呈現。此外，您還會更新履歷，並練習面試技巧，為應徵和面試做好準備。
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Google Cloud 資料分析專業證書課程共有五堂課程，這是第三堂。本課程會先簡要說明資料歷程，從最初的資料收集，一直到最後的洞察分析。接著，您將依序學習如何使用 SQL 將原始資料轉換為可用的格式、怎麼透過資料管道轉換大量資料。最後，您將練習如何將轉換策略應用於實際資料集，滿足業務需求。
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這是 Google Cloud 資料分析專業證書五堂課程中的第二堂。本課程將帶您瞭解資料的結構化和組織方式。您將透過實作，瞭解資料湖倉架構和 BigQuery、Google Cloud Storage 與 DataProc 等雲端元件，有效率地儲存、分析及處理大型資料集。
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這是 Google Cloud Data Analytics 專業證書五堂課程中的第四堂。本課程著重於培養雲端資料圖表製作的技能，共分五個主要階段：敘事、規劃、探索資料、建立圖表，以及與他人分享資料。您還會實際使用 UI/UX 技能，繪製具說服力的雲端原生圖表線框稿，並運用雲端原生資料圖表工具探索資料集、建立報表和資訊主頁，推動決策並促進協作。
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這是 Google Cloud 資料分析專業證書五堂課程中的第一堂。在本課程中，您將瞭解雲端資料分析領域的定義，並說明雲端資料分析師在資料擷取、儲存、處理和視覺化方面的角色與職責。您將瞭解 BigQuery 和 Cloud Storage 等 Google Cloud 工具的架構，以及如何運用這些工具有效組織、呈現及彙整資料。
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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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This is the third of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll begin by getting an overview of the data journey, from collection to insights. You’ll then learn how to use SQL to transform raw data into a usable format. Next, you’ll learn how to transform high volumes of data with a data pipeline. Finally, you’ll gain experience applying transformation strategies to real data sets to solve business needs.
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This is the second of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll explore how data is structured and organized. You’ll gain hands-on experience with the data lakehouse architecture and cloud components like BigQuery, Google Cloud Storage, and DataProc to efficiently store, analyze, and process large datasets.
This is the fifth of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll combine and apply the foundational knowledge and skills from courses 1-4 in a hands-on Capstone project that focuses on the full data lifecycle project. You’ll practice using cloud-based tools to acquire, store, process, analyze, visualize, and communicate data insights effectively. By the end of the course, you’ll have completed a project demonstrating their proficiency in effectively structuring data from multiple sources, presenting solutions to varied stakeholders, and visualizing data insights using cloud-based software. You’ll also update your resume and practice interview techniques to help prepare for applying and interviewing for jobs.
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This is the first of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll define the field of cloud data analysis and describe roles and responsibilities of a cloud data analyst as they relate to data acquisition, storage, processing, and visualization. You’ll explore the architecture of Google Cloud-based tools, like BigQuery and Cloud Storage, and how they are used to effectively structure, present, and report data.
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本課程將介紹 Vertex AI 上的 Generative AI Studio，說明如何用此產品設計生成式 AI 模型的原型及自訂生成式 AI 模型，以利您在應用程式中使用模型的功能。課程中，您可以透過 Generative AI Studio 示範教學逐步認識 Generative AI Studio，並瞭解產品功能、選項與使用方式。課程最後，您可以在實作研究室應用所學，並進行測驗來評估學習成果。
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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。
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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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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This is the fourth of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll focus on developing skills in the five key stages of visualizing data in the cloud: storytelling, planning, exploring data, building visualizations, and sharing data with others. You’ll also gain experience using UI/UX skills to wireframe impactful, cloud-native visualizations and work with cloud-native data visualization tools to explore datasets, create reports, and build dashboards that drive decisions and foster collaboration.
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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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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
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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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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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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This course provides a holistic experience of optimally configuring SAP on Google Cloud. Participants will learn to configure SAP on Google Cloud, and what best practices are, leaving the course with actionable experience to configure SAP on Google Cloud and run SAP workloads on Google Cloud.
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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps.
We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views.
After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets.
This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.
This course, Google Cloud Big Data and Machine Learning Fundamentals - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Google Cloud Big Data and Machine Learning Fundamentals.
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.
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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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會概略介紹雲端基礎知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能闡述這些概念並展示實用技能。學員需依序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
本系列的最後一堂課回顧了代管大數據服務、機器學習與這項技術的價值，以及如何獲得技能徽章，進一步展示您的 Google Cloud 技能。
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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會說明雲端運算基本知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。
完成這一系列課程後，學員將能夠闡述這些概念並展示實用技能。學員應依以下順序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
第三門課涵蓋雲端自動化和管理工具，以及建構安全網路。
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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會概略介紹雲端基礎知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能闡述這些概念並展示實用技能。學員需依序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會說明雲端運算基本知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能夠闡述這些概念並展示實用技能。學員應依以下順序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
第一門課會概略說明雲端運算、Google Cloud 的使用方式，以及不同的運算選項。
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This course, Building Resilient Streaming Analytics Systems on Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Building Resilient Streaming Analytics Systems on Google Cloud.
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners will get hands-on experience building streaming data pipeline components on Google Cloud using QwikLabs.
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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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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 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 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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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 series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
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Earn a skill badge by completing the Explore Machine Learning Models with Explainable AI quest, where you will learn how to do the following using Explainable AI: build and deploy a model to an AI platform for serving (prediction), use the What-If Tool with an image recognition model, identify bias in mortgage data using the What-If Tool, and compare models using the What-If Tool to identify potential bias. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest and the final assessment challenge lab to receive a skill badge that you can share with your network.
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本課程是 Google Cloud 帳單 與費用管理必備知識系列的第二堂 (共兩堂)，最適合從事金融和/或 IT 相關職務， 且負責組織雲端基礎架構最佳化的人士修習。 在這堂課程，您將學會如何控管 Google Cloud 支出並發揮最大效益。 這些做法包括設定預算和警告、管理配額限制 及善用承諾使用折扣。在實作實驗室，你會練習使用各種工具 控管和最佳化 Google Cloud 支出，或引導技術團隊採用最佳做法， 提升資金使用效率。
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本課程最適合從事科技或金融職務， 且負責管理 Google Cloud 費用的人士修習。您將學習如何設定帳單帳戶、 整理資源及管理帳單存取權限。 在實作實驗室，您會瞭解如何查看帳單、使用 BigQuery 或 Google 試算表分析帳單資料， 以及使用 Data Studio 建立自訂的帳單資訊主頁。如需影片提及的參考資源連結， 請參閱其他資源文件。
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"This course, Machine Learning in the Enterprise - Locales, is intended for non-English learners. If you want to take this course in English, please enroll inMachine Learning in the Enterprise". This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks.
The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to e…
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This course, Migrating to Google Cloud - Locales is intended for non-English learners only. To take
course in English, please enroll in Migrating to Google Cloud.
This course introduces participants to the strategies to migrate from
a source environment to Google Cloud. Participants are introduced to Google Cloud's
fundamental concepts and more in depth topics, like creating virtual machines, configuring
networks and managing access and identities. The course then covers the installation
and migration process of Migrate for Compute Engine, including special features
like test clones and wave migrations.
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This course, Achieving Advanced Insights with BigQuery - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Achieving Advanced Insights with BigQuery. The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views. After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.
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完成「設定 Google Cloud 網路」課程，即可獲得技能徽章。 您將瞭解如何在 Google Cloud Platform 執行基本的網路工作，包括建立自訂網路、新增子網路防火牆規則，還有建立 VM 並測試 VM 之間的通訊延遲。
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In this self-paced training course, participants learn mitigations for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. They also learn about the Security Command Center, cloud logging and audit logging, and using Forseti to view overall compliance with your organization's security policies.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Identity, Resource Manager, IAM, Virtual Private Cloud firewalls, Cloud Load Balancing, Cloud Peering, Cloud Interconnect, and VPC Service Controls.
This is the first course of the Security in Google Cloud series. After completing this course, enroll in the Security Best Practices in Google Cloud course.
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task.
Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
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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Introduction to Cloud Identity serves as the starting place for any new Cloud Identity, Identity/Access Management/Mobile Device Management admins as they begin their journey of managing and establishing security and access management best practices for their organization. This 15-30 hour accelerated, one-week course will leave you feeling confident to utilize the basic functions of the Admin Console to manage users, control access to services, configure common security settings, and much more. Through a series of introductory lessons, step-by-step hands-on exercises, Google knowledge resources, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as new Cloud Identity Administrators.
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Google Cloud Fundamentals for AWS Professionals introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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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 fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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In this quest you will use a collection of Google APIs that are all related to language, and speech. You will use the Speech-to-Text API to transcribe an audio file into a text file, the Cloud Translation API to translate from one language to another, the Cloud Translation API to detect what language is being used and translate to a different language, the Natural Language API to classify text and analyze sentiment, and create synthetic speech.
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"This course, Machine Learning in the Enterprise - Locales, is intended for non-English learners. If you want to take this course in English, please enroll inMachine Learning in the Enterprise". This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks.
The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to e…
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"This course, Feature Engineering - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Feature Engineering."
Want to know about Vertex AI Feature Store? Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering, where we discuss good versus bad features and how you can preprocess and transform them for optimal use in your models. This course includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow
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This course, TensorFlow on Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in TensorFlow on Google Cloud.
This course covers designing and building a TensorFlow 2.x input data pipeline, building ML models with TensorFlow 2.x and Keras, improving the accuracy of ML models, writing ML models for scaled use and writing specialized ML models.
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
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大家都知道，機器學習是發展最快的科技領域之一， 而 Google Cloud Platform 在這方面功不可沒。 GCP 提供多種 API，凡是與機器學習相關的任務，幾乎都能處理。您將在本入門課程的 實驗室，實際演練機器學習技術 在語言處理方面的應用，學會如何從文中擷取實體資訊、 執行情緒和語法分析，並使用 Speech-to-Text API 轉錄語音。
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大數據、機器學習和人工智慧 (AI) 是時下熱門的 電腦相關話題，但這些領域相當專業，就算想要入門 也難以取得教材或資料。幸好，Google Cloud 提供了此領域的多種服務，而且容易使用。 參加這堂入門課程，您就能踏出第一步， 開始學習運用 BigQuery、Cloud Speech API 以及 Video Intelligence 等工具。
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
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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, How Google Does Machine Learning- Locales, is intended for non-English learners. If you want to take this course in English, please enroll in How Google Does Machine Learning.
What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve?
Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and bat…
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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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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 使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL 技能徽章入門課程，證明您具備下列技能： 使用「資料庫遷移服務」中各種可用的工作類型和連線選項， 將 MySQL 資料遷移至 Cloud SQL，以及在執行「資料庫遷移服務」工作時 遷移 MySQL 使用者資料。
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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.
Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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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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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
如果您是剛起步的雲端開發人員， 想在 Google Cloud Essentials 外獲得更多實作經驗，歡迎參加本課程。您將透過實作實驗室， 深入瞭解 Cloud Storage 和其他重要應用程式服務，例如： Monitoring 和 Cloud Functions。您將習得 在任何 Google Cloud 專案都適用的寶貴技能。
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Google Cloud 的服務在安全上絕不妥協， 因此開發了專用工具，確保所有專案安全無虞， 使用者也能妥善管理身分識別機制。在這堂入門課程中，您會實際使用 Google Cloud 的 Identity and Access Management (IAM) 服務， 練習管理使用者和虛擬機器帳戶。您將 佈建虛擬私有雲和 VPN 來熟悉網路安全功能，並瞭解有哪些工具 可防範資安威脅和資料遺失。
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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
This course offers hands-on practice with migrating MySQL data to Cloud SQL using Database Migration Service. You start with an introductory lab that briefly reviews how to get started with Cloud SQL for MySQL, including how to connect to Cloud SQL instances using the Cloud Console. Then, you continue with two labs focused on migrating MySQL databases to Cloud SQL using different job types and connectivity options available in Database Migration Service. The course ends with a lab on migrating MySQL user data when running Database Migration Service jobs.
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本入門課程有別於其他課程。 透過這些實驗室，IT 專業人員將有機會實際練習， 熟悉出現在 Google Cloud 助理雲端工程師認證中的主題和服務。本課程包含多個專門的實驗室，從 IAM、網路建立 到 Kubernetes Engine 部署作業， 可全面驗收您的 Google Cloud 知識。請注意，雖然進行這些 實驗室可提升您的技能和能力，但仍建議同時詳閱 測驗指南和其他可用的準備資源。
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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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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
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本課程提供 Cloud Data Fusion 的實作練習。這是一款雲端原生、 無程式碼的資料整合平台。ETL 開發人員、資料工程師和分析師 可運用預先建立的轉換和連接器， 輕鬆建構及部署管道，不必擔心編寫程式碼。本課程會以快速入門實驗室拉開序幕， 讓學員熟悉 Cloud Data Fusion UI，接著嘗試執行批次和即時管道， 以及使用內建 Wrangler 外掛程式， 對資料執行有趣的轉換。
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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「在 Google Cloud 使用機器學習 API」課程，即可獲得進階技能徽章。本課程說明以下機器學習和 AI 技術的基本功能： Cloud Vision API、Cloud Translation API 和 Cloud Natural Language API。
企業 AI 代理與用途
Earned Mar 1, 2026 EST
生成式 AI 代理：實現組織轉型
Earned Dec 11, 2025 EST
綜合應用：為 Cloud 資料分析師工作做準備
Earned Nov 18, 2025 EST
在 Cloud 轉換資料
Earned Nov 18, 2025 EST
在 Cloud 管理與儲存資料
Earned Nov 18, 2025 EST
資料敘事的強大力量：如何在 Cloud 以圖表呈現資料
Earned Nov 18, 2025 EST
Google Cloud 資料分析功能簡介
Earned Nov 18, 2025 EST
透過 Vertex AI 建構及部署機器學習解決方案
Earned Dec 12, 2024 EST
Data Transformation in the Cloud
Earned Aug 29, 2024 EDT
Data Management and Storage in the Cloud
Earned Aug 20, 2024 EDT
使用 Gemini 多模態功能和多模態 RAG 檢查複合型文件
Earned Aug 18, 2024 EDT
Put It All Together: Prepare for a Cloud Data Analyst Job
Earned Aug 13, 2024 EDT
Introduction to Data Analytics in Google Cloud
Earned Aug 9, 2024 EDT
Introduction to Generative AI Studio - 繁體中文
Earned Aug 4, 2024 EDT
Generative AI Fundamentals - 繁體中文
Earned Aug 4, 2024 EDT
在 Agent Platform 設計提示
Earned Aug 4, 2024 EDT
負責任的 AI 技術簡介
Earned Aug 2, 2024 EDT
生成式 AI 簡介
Earned Aug 2, 2024 EDT
大型語言模型簡介
Earned Aug 1, 2024 EDT
The Power of Storytelling: How to Visualize Data in the Cloud
Earned Aug 1, 2024 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Dec 6, 2022 EST
[DEPRECATED] Data Engineering
Earned Dec 5, 2022 EST
Modernize Infrastructure and Applications with Google Cloud
Earned Dec 4, 2022 EST
Understanding LookML in Looker
Earned Dec 2, 2022 EST
Developing a Google SRE Culture
Earned Nov 30, 2022 EST
Deploying SAP on Google Cloud
Earned Nov 29, 2022 EST
Applying Machine Learning to your Data with Google Cloud
Earned Nov 16, 2022 EST
Achieving Advanced Insights with BigQuery
Earned Nov 15, 2022 EST
Exploring and Preparing your Data with BigQuery
Earned Nov 8, 2022 EST
建立及管理 Bigtable 執行個體
Earned Nov 5, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Locales
Earned Nov 4, 2022 EDT
建立及管理 Cloud Spanner 執行個體
Earned Nov 3, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Nov 2, 2022 EDT
Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
Earned Nov 1, 2022 EDT
Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
Earned Oct 31, 2022 EDT
Google Cloud 運算的基本概念：Google Cloud 基礎架構
Earned Oct 30, 2022 EDT
Google Cloud 運算的基本概念：Cloud 運算基礎知識
Earned Oct 29, 2022 EDT
Building Resilient Streaming Analytics Systems on Google Cloud - Locales
Earned Oct 27, 2022 EDT
Recommendation Systems on Google Cloud
Earned Oct 26, 2022 EDT
Machine Learning in the Enterprise
Earned Oct 24, 2022 EDT
Natural Language Processing on Google Cloud
Earned Oct 22, 2022 EDT
Production Machine Learning Systems
Earned Oct 21, 2022 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Aug 6, 2022 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Jul 28, 2022 EDT
Deprecated : Managing Machine Learning Projects with Google Cloud
Earned Apr 11, 2022 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI
Earned Apr 9, 2022 EDT
讓您的 Google Cloud 支出發揮最大效益
Earned Feb 2, 2022 EST
瞭解 Google Cloud 費用
Earned Feb 2, 2022 EST
Machine Learning in the Enterprise - Locales
Earned Feb 1, 2022 EST
Migrating to Google Cloud - Locales
Earned Feb 1, 2022 EST
Achieving Advanced Insights with BigQuery - Locales
Earned Jan 31, 2022 EST
Security Best Practices in Google Cloud
Earned Jan 30, 2022 EST
設定 Google Cloud 網路
Earned Jan 29, 2022 EST
Mitigating Security Vulnerabilities on Google Cloud
Earned Jan 24, 2022 EST
Managing Security in Google Cloud
Earned Jan 18, 2022 EST
Scaling with Google Cloud Operations
Earned Jan 17, 2022 EST
Introduction to Cloud Identity
Earned Jan 16, 2022 EST
Google Cloud Fundamentals for AWS Professionals
Earned Jan 14, 2022 EST
Google Cloud 基礎知識：核心基礎架構
Earned Jan 14, 2022 EST
DEPRECATED Cloud Architecture
Earned Jan 11, 2022 EST
DEPRECATED IoT in the Google Cloud
Earned Jan 10, 2022 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Jan 9, 2022 EST
Scientific Data Processing
Earned Jan 8, 2022 EST
DEPRECATED Language, Speech, Text, & Translation with Google Cloud APIs
Earned Jan 5, 2022 EST
Machine Learning in the Enterprise - Locales
Earned Jan 3, 2022 EST
Feature Engineering - Locales
Earned Jan 2, 2022 EST
TensorFlow on Google Cloud - Locales
Earned Jan 1, 2022 EST
Intro to ML: Image Processing
Earned Dec 31, 2021 EST
機器學習簡介：語言處理
Earned Dec 29, 2021 EST
基本概念：資料、機器學習和 AI
Earned Dec 28, 2021 EST
Data Science on Google Cloud
Earned Dec 26, 2021 EST
Launching into Machine Learning
Earned Dec 25, 2021 EST
How Google Does Machine Learning - Locales
Earned Dec 22, 2021 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Dec 19, 2021 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Dec 16, 2021 EST
使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL
Earned Dec 14, 2021 EST
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned Dec 12, 2021 EST
使用 BigQuery 進行機器學習
Earned Dec 11, 2021 EST
DEPRECATED BigQuery for Marketing Analysts
Earned Dec 9, 2021 EST
Data Catalog Fundamentals
Earned Dec 7, 2021 EST
DEPRECATED BigQuery Basics for Data Analysts
Earned Dec 6, 2021 EST
DEPRECATED BigQuery for Data Warehousing
Earned Dec 5, 2021 EST
透過 BigQuery 建構資料倉儲
Earned Dec 3, 2021 EST
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Dec 2, 2021 EST
基本概念：基礎架構
Earned Nov 30, 2021 EST
安全性與身分識別基礎知識
Earned Nov 29, 2021 EST
Machine Learning APIs
Earned Nov 27, 2021 EST
雲端架構：設計、實作與管理
Earned Nov 23, 2021 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned Nov 21, 2021 EST
雲端工程
Earned Nov 19, 2021 EST
Google Cloud 必備知識
Earned Nov 17, 2021 EST
Cloud SQL
Earned Nov 15, 2021 EST
在 Cloud Data Fusion 建構免程式碼管道
Earned Nov 8, 2021 EST
運用 BigQuery ML 建立機器學習模型
Earned Nov 2, 2021 EDT
在 Google Cloud 使用機器學習 API
Earned Oct 29, 2021 EDT
建立 Google Cloud 網路
Earned Oct 18, 2021 EDT
從 BigQuery 資料取得深入分析結果
Earned Oct 16, 2021 EDT
在 Google Cloud 設定應用程式開發環境
Earned Oct 3, 2021 EDT
為 Looker 資訊主頁和報表準備資料
Earned Sep 12, 2021 EDT
在 Compute Engine 導入 Cloud Load Balancing
Earned Sep 6, 2021 EDT