Vertex AI is now Gemini Enterprise Agent Platform! We are currently updating our content to reflect this change. Please bear with us if you encounter naming inconsistencies during this transition.
在 Google Cloud 控制台中运用您的技能
Jorge Alberto Zúñiga Herrera
成为会员时间：2021
企业智能体及其应用场景
Earned Mar 1, 2026 EST
生成式 AI 智能体：助力组织转型
Earned Dec 11, 2025 EST
融会贯通：为云数据分析师岗位做好准备
Earned Nov 18, 2025 EST
云端数据转换
Earned Nov 18, 2025 EST
云端数据管理和存储
Earned Nov 18, 2025 EST
讲故事的力量：如何在云端可视化数据
Earned Nov 18, 2025 EST
Google Cloud 数据分析功能简介
Earned Nov 18, 2025 EST
在 Vertex AI 上构建和部署机器学习解决方案
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
使用多模态 Gemini 和多模态 RAG 检查富文档
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 - 简体中文
Earned Aug 4, 2024 EDT
Generative AI Fundamentals - 简体中文
Earned Aug 4, 2024 EDT
在 Agent Platform 中设计提示
Earned Aug 4, 2024 EDT
负责任的 AI 简介
Earned Aug 2, 2024 EDT
生成式 AI 简介
Earned Aug 2, 2024 EDT
大型语言模型简介
Earned Aug 1, 2024 EDT
The Power of Storytelling: How to Visualize Data in the Cloud
Earned Aug 1, 2024 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
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
创建和管理 Bigtable 实例
Earned Nov 5, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Locales
Earned Nov 4, 2022 EDT
创建和管理 Cloud Spanner 实例
Earned Nov 3, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Nov 2, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI
Earned Nov 1, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性
Earned Oct 31, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的基础设施
Earned Oct 30, 2022 EDT
Google Cloud 云计算基础课程：云计算基础知识
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 上为机器学习 API 准备数据
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
优化 Google Cloud 成本
Earned Feb 2, 2022 EST
了解自己的 Google Cloud 成本
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
设置 Google Cloud 网络
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 基础知识：核心基础设施
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
机器学习简介：语言处理
Earned Dec 29, 2021 EST
基准：数据、机器学习和 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
用 Database Migration Service 将 MySQL 数据迁移至 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
面向机器学习的 BigQuery
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
使用 BigQuery 构建数据仓库
Earned Dec 3, 2021 EST
在 Google Cloud 上实施云安全基础措施
Earned Dec 2, 2021 EST
基准：基础架构
Earned Nov 30, 2021 EST
安全与身份基础知识
Earned Nov 29, 2021 EST
Machine Learning APIs
Earned Nov 27, 2021 EST
云架构：设计、实施和管理
Earned Nov 23, 2021 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned Nov 21, 2021 EST
云工程
Earned Nov 19, 2021 EST
Google Cloud 基础知识
Earned Nov 17, 2021 EST
Cloud SQL
Earned Nov 15, 2021 EST
在 Cloud Data Fusion 上构建无代码流水线
Earned Nov 8, 2021 EST
通过 BigQuery ML 创建机器学习模型
Earned Nov 2, 2021 EDT
在 Google Cloud 上使用 Machine Learning API
Earned Oct 29, 2021 EDT
开发 Google Cloud 网络
Earned Oct 18, 2021 EDT
从 BigQuery 数据中挖掘数据洞见
Earned Oct 16, 2021 EDT
在 Google Cloud 上设置应用开发环境
Earned Oct 3, 2021 EDT
为 Looker 信息中心和报告准备数据
Earned Sep 12, 2021 EDT
为 Compute Engine 实现云负载均衡
Earned Sep 6, 2021 EDT
了解 AI 智能体如何为业务带来实际影响。您将把智能体类型关联到您的关键绩效指标 (KPI)，并探索能够解决实际瓶颈的用例。然后，您将了解 Gemini Enterprise 如何帮助您构建和编排合适的智能体，其范围涵盖从无代码到高代码的各种解决方案。
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“生成式 AI 智能体：助力组织转型”是“Gen AI Leader”学习路线中的第五门课程，也是最后一门课程。本课程探讨了组织如何使用量身定制的生成式 AI 智能体，帮助应对特定的业务挑战。您将亲自动手构建一个基本的生成式 AI 智能体，并探索这些智能体的组成部分，例如模型、推理循环以及各种工具。
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本课程是 Google Cloud 数据分析认证计划的第五门课程（共五门）。在本课程中，你将综合运用前 4 门课程所学的基础知识和技能，实操完成一个结业项目，全面探索整个数据生命周期。您将练习使用云端工具来有效地获取、存储、处理、分析、直观呈现数据并传达数据分析洞见。课程结束时，您将完成一个项目，证明您在以下方面的熟练程度：高效地设计数据结构以整理来自多个来源的数据、向不同利益相关方展示解决方案，以及使用云端软件直观呈现数据分析洞见。您还将更新个人简历并练习面试技巧，为求职申请与面试环节做好准备。
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本课程是 Google Cloud 数据分析认证计划的第三门课程（共五门）。在本课程中，您将首先了解从收集数据到获取数据分析洞见的整个数据历程。然后，您将学习如何使用 SQL 将原始数据转换为可用格式。接下来，您将学习如何使用数据流水线转换大量数据。最后，您会获得相关经验，熟悉如何将数据转换策略应用于真实数据集以满足业务需求。
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本课程是 Google Cloud 数据分析认证计划的第二门课程（共五门）。在本课程中，您将探索数据的结构形式和组织方式。您将获得数据湖仓一体架构和云组件（如 BigQuery、Google Cloud Storage 和 DataProc）的实操经验，以便高效地存储、分析和处理大型数据集。
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本课程是 Google Cloud 数据分析认证计划的第四门课程（共五门课程）。在本课程中，您将重点学习在云端可视化数据的相关技能，其中数据可视化可分为五个关键阶段：讲故事、规划、探索数据、构建可视化图表以及与他人共享数据。您还将获得实操经验，尝试使用 UI（界面）/UX（用户体验）技能来制作线框图，从而设计出有影响力的云原生可视化图表，并使用云原生数据可视化工具来探索数据集、创建报告和构建信息中心，从而推动决策并促进协作。
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本课程是 Google Cloud 数据分析认证的第一门课程（共五门）。在本课程中，您将认识云数据分析领域，并了解云数据分析师在数据获取、存储、处理和可视化方面的角色和职责。您将探索 BigQuery 和 Cloud Storage 等基于 Google Cloud 的工具的架构，以及如何使用这些工具有效地设计数据结构，以及展示和报告数据。
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完成在 Vertex AI 上构建和部署机器学习解决方案课程，赢取中级技能徽章。 在此课程中，您将了解如何使用 Google Cloud 的 Vertex AI Platform、AutoML 以及自定义训练服务来 训练、评估、调优、解释和部署机器学习模型。 此技能徽章课程的目标受众是专业的数据科学家和机器学习 工程师。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您对 Google Cloud 产品与服务的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成此技能徽章课程 和作为最终评估的实验室挑战赛，即可获得数字徽章， 在您的人际圈中炫出自己的技能。
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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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本课程介绍一款基于 Vertex AI 技术的产品 Generative AI Studio，它可帮助您开发生成式 AI 模型的原型并进行自定义，从而支持您在自己的应用中使用其功能。在本课程中，您将通过 Generative AI Studio 产品的演示，了解该产品是什么，提供什么功能和选项，以及如何使用。最后，您将完成一个实操实验应用所学知识，并通过一个测验来检查知识掌握情况。
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完成 Introduction to Generative AI、Introduction to Large Language Models 和 Introduction to Responsible AI 三门课程，赢取技能徽章。通过最终测验，即表明您理解了生成式 AI 的基本概念。
技能徽章是由 Google Cloud 颁发的数字徽章，旨在认可您对 Google Cloud 产品与服务的了解程度。公开您的个人资料并将技能徽章添加到您的社交媒体个人资料中，以此来分享您获得的成就。
这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。
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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
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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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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。
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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 云计算基础课程面向没有或很少有云计算基础或经验的人群。本课程概述了云计算基础知识、大数据和机器学习的核心概念，以及 Google Cloud 在其中的定位与应用方式。
完成本系列课程后，学员将能够清晰阐述这些概念，并掌握一些实际操作技能。
课程应按以下顺序完成：
1. Google Cloud 云计算基础课程：云计算基础知识
2. Google Cloud 云计算基础课程：Google Cloud 中的基础设施
3. Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性
4. Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI
本课程是该系列课程的最后一门，回顾了托管式大数据服务、机器学习及其价值，以及如何通过获得技能徽章来进一步展示您在 Google Cloud 方面的技能。
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Google Cloud 云计算基础课程面向几乎没有云计算背景或经验的人士。本课程概述了云计算基础知识、大数据和机器学习的核心概念，以及 Google Cloud 在其中的定位与应用方式。
完成本课程系列后，学员将能够阐述这些概念，并展示一定的实操技能。
课程应按以下顺序完成：
1. Google Cloud 云计算基础课程：云计算基础知识
2. Google Cloud 云计算基础课程：Google Cloud 中的基础设施
3. Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性
4. Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI
本课是第三门课程，介绍云端自动化和管理工具以及如何构建安全网络。
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Google Cloud 云计算基础课程面向云计算零基础或经验较少的人群。本课程概述了云计算基础知识、大数据和机器学习的核心概念，以及 Google Cloud 在其中的定位与应用方式。
完成本系列课程后，学员将能够清晰阐述这些概念，并掌握部分实操技能。
课程应按以下顺序完成：
1. Google Cloud 云计算基础课程：云计算基础知识
2. Google Cloud 云计算基础课程：Google Cloud 中的基础设施
3. Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性
4. Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI
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Google Cloud 云计算基础课程面向云计算零基础或经验较少的人群。本课程概述了云计算基础知识、大数据和机器学习的核心概念，以及 Google Cloud 在其中的定位与应用方式。
完成本系列课程后，学员将能够清晰阐述这些概念，并掌握部分实操技能。
课程应按以下顺序完成：
1. Google Cloud 云计算基础课程：云计算基础知识
2. Google Cloud 云计算基础课程：Google Cloud 中的基础设施
3. Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性
4. Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI
本课是第一门课程，概述了云计算、Google Cloud 的使用方式以及各种计算选项。
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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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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Managed Service for Apache Spark 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 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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Google Cloud 结算和费用管理基础知识系列课程包含两部分内容， 本课是第二课。本课程最适合担任财务和/或 IT 职务 且负责优化所在组织的云基础设施的人员。 在这门课程中，您将学习多种控制和优化 Google Cloud 成本的方式， 包括设置预算和提醒、管理配额限制以及充分利用 承诺使用折扣。在实操实验中，您将练习使用各种 工具控制和优化 Google Cloud 成本，或者影响技术 团队应用成本优化最佳实践。
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本课程最适合担任技术或财务职务 且负责管理 Google Cloud 费用的人员。您将学习如何设置 结算账号、组织资源以及管理结算访问权限。 在实操实验中，您将学习如何查看账单、使用 BigQuery 或 Google 表格分析结算数据， 以及使用 Data Studio 创建自定义结算信息中心。视频中提及的链接 可以在此其他资源文档中查看。
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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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完成设置 Google Cloud 网络课程，赢取技能徽章， 您将了解如何在 Google Cloud Platform 上执行基本的网络组建和管理任务 - 创建自定义网络、添加子网防火墙规则，然后创建虚拟机并测试 虚拟机之间相互通信时的延迟时间。
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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 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。
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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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众所周知，机器学习是发展最快的技术领域之一， Google Cloud Platform 在推动其发展方面发挥了重要作用。 GCP 提供了一系列 API，几乎可以满足任何机器学习作业的需求。在 本入门课程中，您将了解机器学习在语言处理方面的运用， 通过实操实验学习 如何从文本中提取实体，执行情感和语法分析，以及 使用 Speech-to-Text API 进行转写。
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大数据、机器学习和人工智能是当今计算领域的热门话题， 但这些领域的专业性很强，因而很难找到 入门资料。幸运的是，Google Cloud 在这些领域提供了方便用户使用的服务， 通过本入门级课程，您可以 开始学习使用 BigQuery、Cloud Speech API 和 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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完成用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL 这一入门级的技能徽章课程，展示您在以下方面的技能： 使用 Database Migration Service 中提供的不同作业类型和连接选项，将 MySQL 数据迁移到 Cloud SQL； 以及在运行 Database Migration Service 作业时 迁移 MySQL 用户数据。
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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.
安全是 Google Cloud 服务绝不妥协的核心原则，为此， Google Cloud 开发了特定工具，为您的所有项目提供 安全与身份保障。本入门课程中，您将了解 用于管理用户和虚拟机账号的 Google Cloud Identity and Access Management (IAM) 服务 并进行实操练习。您还将通过 配置 VPC 和 VPN 获取网络安全方面的实践经验，并了解有哪些工具可用于 抵御安全威胁和防止数据泄露。
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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.
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完成 云架构：设计、实施和管理课程，赢取技能徽章，展示您在以下方面的技能：使用 Apache Web 服务器部署可公开访问的网站；使用启动脚本配置 Compute Engine 虚拟机； 使用 Windows 堡垒主机和防火墙规则配置安全 RDP；构建 Docker 映像并将其部署到 Kubernetes 集群，然后进行更新；以及创建 CloudSQL 实例并导入 MySQL 数据库。 此技能徽章课程是非常有用的资源， 可帮助您理解 Google Cloud 认证 Professional Cloud Architect 认证考试中将会出现的主题。
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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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在众多课程中，本入门课程独具特色。 这些实验经过精心设计，旨在让 IT 专业人员通过实践掌握 Google Cloud 认证 Associate Cloud Engineer 考核中的各项主题和服务内容。从 IAM 到网络组建和管理， 再到 Kubernetes Engine 部署，本课程将通过特定实验 检验您的 Google Cloud 知识掌握情况。请注意，虽然这些实操 实验有助于提升您的技能和能力，我们仍建议您同时查阅 考试指南和其他可用的备考资源。
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在本入门级课程中，您将了解 Google Cloud 的基础工具和服务。此课程提供了可选视频， 旨在帮助您深入了解和回顾实验中涉及的概念。Google Cloud 基础知识是推荐给 Google Cloud 学员的第一门课程 - 即使您几乎没有云相关知识，也能从中获得实践 经验，并将其直接运用于您的首个 Google Cloud 项目。从编写 Cloud Shell 命令和部署您的第一个虚拟机，到在 Kubernetes Engine 上运行应用 或者使用负载均衡，“Google Cloud 基础知识”都是您了解该平台 基本功能的首选入门级课程。
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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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本课程提供 Cloud Data Fusion 的实操练习。Cloud Data Fusion 是云原生的 无代码数据集成平台。受益于此，ETL 开发者、数据工程师和分析师 可以利用预构建的转换工具和连接器，轻松构建和 部署流水线，而无需操心编码问题。本课程首先提供了 一个快速入门实验，让学习者熟悉 Cloud Data Fusion 界面。接下来， 学员将尝试运行批处理和实时流水线，并使用内置的 Wrangler 插件对数据执行一些有趣的转换。
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完成中级技能徽章课程通过 BigQuery ML 创建机器学习模型，展示您在以下方面的技能： 使用 BigQuery ML 创建和评估机器学习模型，以执行数据预测。
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完成在 Google Cloud 上使用 Machine Learning API 课程，赢取高级技能徽章。 在本课程中，您将了解以下机器学习和 AI 技术的基本功能： Cloud Vision API、Cloud Translation API 和 Cloud Natural Language API。
企业智能体及其应用场景
Earned Mar 1, 2026 EST
生成式 AI 智能体：助力组织转型
Earned Dec 11, 2025 EST
融会贯通：为云数据分析师岗位做好准备
Earned Nov 18, 2025 EST
云端数据转换
Earned Nov 18, 2025 EST
云端数据管理和存储
Earned Nov 18, 2025 EST
讲故事的力量：如何在云端可视化数据
Earned Nov 18, 2025 EST
Google Cloud 数据分析功能简介
Earned Nov 18, 2025 EST
在 Vertex AI 上构建和部署机器学习解决方案
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
使用多模态 Gemini 和多模态 RAG 检查富文档
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 - 简体中文
Earned Aug 4, 2024 EDT
Generative AI Fundamentals - 简体中文
Earned Aug 4, 2024 EDT
在 Agent Platform 中设计提示
Earned Aug 4, 2024 EDT
负责任的 AI 简介
Earned Aug 2, 2024 EDT
生成式 AI 简介
Earned Aug 2, 2024 EDT
大型语言模型简介
Earned Aug 1, 2024 EDT
The Power of Storytelling: How to Visualize Data in the Cloud
Earned Aug 1, 2024 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
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
创建和管理 Bigtable 实例
Earned Nov 5, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Locales
Earned Nov 4, 2022 EDT
创建和管理 Cloud Spanner 实例
Earned Nov 3, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Nov 2, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI
Earned Nov 1, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性
Earned Oct 31, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的基础设施
Earned Oct 30, 2022 EDT
Google Cloud 云计算基础课程：云计算基础知识
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 上为机器学习 API 准备数据
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
优化 Google Cloud 成本
Earned Feb 2, 2022 EST
了解自己的 Google Cloud 成本
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
设置 Google Cloud 网络
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 基础知识：核心基础设施
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
机器学习简介：语言处理
Earned Dec 29, 2021 EST
基准：数据、机器学习和 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
用 Database Migration Service 将 MySQL 数据迁移至 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
面向机器学习的 BigQuery
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
使用 BigQuery 构建数据仓库
Earned Dec 3, 2021 EST
在 Google Cloud 上实施云安全基础措施
Earned Dec 2, 2021 EST
基准：基础架构
Earned Nov 30, 2021 EST
安全与身份基础知识
Earned Nov 29, 2021 EST
Machine Learning APIs
Earned Nov 27, 2021 EST
云架构：设计、实施和管理
Earned Nov 23, 2021 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned Nov 21, 2021 EST
云工程
Earned Nov 19, 2021 EST
Google Cloud 基础知识
Earned Nov 17, 2021 EST
Cloud SQL
Earned Nov 15, 2021 EST
在 Cloud Data Fusion 上构建无代码流水线
Earned Nov 8, 2021 EST
通过 BigQuery ML 创建机器学习模型
Earned Nov 2, 2021 EDT
在 Google Cloud 上使用 Machine Learning API
Earned Oct 29, 2021 EDT
开发 Google Cloud 网络
Earned Oct 18, 2021 EDT
从 BigQuery 数据中挖掘数据洞见
Earned Oct 16, 2021 EDT
在 Google Cloud 上设置应用开发环境
Earned Oct 3, 2021 EDT
为 Looker 信息中心和报告准备数据
Earned Sep 12, 2021 EDT
为 Compute Engine 实现云负载均衡
Earned Sep 6, 2021 EDT