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Jayesh Malkolikar

成为会员时间：2021

白银联赛

41325 积分
Getting Started with Google Cloud VMware Engine Earned May 29, 2025 EDT
使用 Vertex AI 中的 Gemini API 探索生成式 AI Earned Dec 1, 2024 EST
Text Prompt Engineering Techniques Earned May 2, 2024 EDT
适用于 DevOps 工程师的 Gemini Earned May 1, 2024 EDT
Generative AI for Business Leaders Earned Apr 30, 2024 EDT
使用 Google Cloud Observability 进行监控和记录 Earned Apr 13, 2024 EDT
Visualize Your Data in Looker Earned Apr 10, 2024 EDT
BI Reporting: Looker Visualization on BigQuery Earned Apr 5, 2024 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos Earned Apr 3, 2024 EDT
Improving developer velocity with Gemini Code Assist Earned Mar 29, 2024 EDT
Build Batch Data Pipelines on Google Cloud Earned Mar 25, 2024 EDT
Migrating Hadoop Workloads Earned Mar 24, 2024 EDT
在 Vertex AI 中设计提示 Earned Mar 19, 2024 EDT
Developing a Google SRE Culture Earned Mar 13, 2024 EDT
Getting Started with Terraform for Google Cloud Earned Mar 10, 2024 EDT
Google Meet 中的 Gemini Earned Feb 5, 2024 EST
Google 幻灯片中的 Gemini Earned Feb 5, 2024 EST
Google 表格中的 Gemini Earned Feb 5, 2024 EST
Google 文档中的 Gemini Earned Feb 4, 2024 EST
Gmail 中的 Gemini Earned Feb 4, 2024 EST
Gemini for Google Workspace 简介 Earned Feb 4, 2024 EST
Google API products: A key to modern application development Earned Jan 11, 2024 EST
Text Prompt Engineering Techniques Earned Aug 13, 2023 EDT
Implementing Generative AI with Vertex AI Earned Aug 13, 2023 EDT
Vertex AI Studio 简介 Earned Aug 10, 2023 EDT
创建图片标注模型 Earned Aug 10, 2023 EDT
Transformer 模型和 BERT 模型 Earned Aug 10, 2023 EDT
编码器-解码器架构 Earned Aug 10, 2023 EDT
注意力机制 Earned Aug 10, 2023 EDT
Generative AI Explorer : Vertex AI Earned Aug 7, 2023 EDT
图像生成简介 Earned Jul 30, 2023 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则 Earned Jul 30, 2023 EDT
Generative AI Fundamentals Earned Jul 24, 2023 EDT
负责任的 AI 简介 Earned Jul 22, 2023 EDT
大型语言模型简介 Earned Jul 22, 2023 EDT
生成式 AI 简介 Earned Jul 22, 2023 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理 Earned Jun 3, 2023 EDT
开发 Google Cloud 网络 Earned Jun 1, 2023 EDT
Google Cloud 弹性基础设施：扩缩和自动化 Earned Jun 1, 2023 EDT
可靠的 Google Cloud 基础设施: 设计和流程 Earned May 27, 2023 EDT
使用 BigQuery 构建数据仓库 Earned May 27, 2023 EDT
Google Cloud 重要基础设施：基础 Earned May 26, 2023 EDT
Google Cloud 重要基础设施：核心服务 Earned May 20, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned May 19, 2023 EDT
Preparing for your Professional Cloud Architect Journey Earned May 2, 2023 EDT
Preparing for your Professional Data Engineer Journey Earned Sep 17, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 3, 2022 EDT
Serverless Data Processing with Dataflow: Foundations Earned Jul 22, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Jul 22, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Jul 21, 2022 EDT
Build Streaming Data Pipelines on Google Cloud Earned Jul 20, 2022 EDT
为 Compute Engine 实现云负载均衡 Earned Jan 3, 2022 EST
在 Google Cloud 上设置应用开发环境 Earned Dec 29, 2021 EST
Preparing for Your Associate Cloud Engineer Journey Earned Dec 27, 2021 EST

This workload aims to upskill Google Cloud Partners about what Google Cloud VMware Engine is, design considerations, and basic implementation. It will also cover interoperability with on-premises vSphere and real world use cases for deployment. Finally, it will cover how VMware Engine compares to other cloud providers' offerings of VMware as a Service and future plans for VMware Engine.

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完成中级技能徽章课程使用 Vertex AI 中的 Gemini API 探索生成式 AI，展示自己在以下方面的技能： 文本生成技能、用于增强内容创作能力的图像和视频分析技能，以及在 Gemini API 中应用函数调用技术的技能。 了解如何运用先进的 Gemini 技术、探索多模态内容生成方法，并扩展 AI 赋能项目的功能。

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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.

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在本课程中，您将了解 Gemini（Google Cloud 推出的一款依托生成式 AI 的协作工具）如何帮助工程师管理基础设施。您将了解如何向 Gemini 输入提示，让其查找和理解应用日志、创建 GKE 集群，以及研究如何创建构建环境。您可以通过实操实验了解如何利用 Gemini 来改进 DevOps 工作流。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.

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完成入门级使用 Google Cloud Observability 进行监控和记录技能徽章课程， 展示自己在以下方面的技能：监控 Compute Engine 中的虚拟机； 利用 Cloud Monitoring 监控多个项目；将监控和日志记录功能扩展到 Cloud Functions； 创建和发送自定义应用指标；以及根据自定义指标配置 Cloud Monitoring 提醒。

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This skill badge course aims to unlock the power of data visualization and business intelligence reporting with Looker, and gain hands-on experience through labs.

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This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.

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Course two of the Architecting Hybrid Cloud with Anthos series prepares students to operate and observe Anthos environments. Through presentations and hands-on labs, participants explore adjusting existing clusters, setting up advanced traffic routing policies, securing communication across workloads, and observing clusters in Anthos. This course is a continuation of course one, Multi-Cluster, Multi-Cloud with Anthos, and assumes direct experience with the topics covered in that course.

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Learn how Gemini can revolutionize your ability to develop applications! This course helps developers go beyond the basics and learn how to integrate Gemini into their workflows.

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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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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating workloads from Hadoop environments to corresponding Google Cloud services and hosted products. The following will addressed will be: The Hadoop ecosystem and products Hadoop architecture and post migration architectures to Google Cloud Assessment Data transfer options Workload migrations, namely: Spark to Dataproc Serverless, Apache Oozie to Composer (Airflow), and Hive to BigQuery Security and governance Logging and Monitoring

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完成 在 Vertex AI 中设计提示入门技能徽章课程，展示以下方面的技能： Vertex AI 中的提示工程、图片分析和多模态生成式技术。探索如何编写有效的提示，指导生成式 AI 输出， 以及将 Gemini 模型应用于真实的营销场景。

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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 an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure. Learners will get hands-on practice building and managing Google Cloud resources using Terraform.

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Google Workspace 专用 Gemini 是一个插件，可为用户提供对生成式 AI 功能的访问权限。本课程深入探讨了“Google Meet 中的 Gemini”的功能。通过视频课程、实操活动和实际示例，您将全面了解 Google Meet 中的 Gemini 功能。您将学习如何使用 Gemini 生成背景图片、提高视频质量以及翻译字幕。学完本课程后，您将掌握相关知识和技能，能够自信地利用 Google Meet 中的 Gemini 尽可能提高视频会议的效率。

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Google Workspace 专用 Gemini 是一个插件，可在 Google Workspace 中为客户提供生成式 AI 功能。在本迷你课程中，您将了解 Gemini 的主要功能，以及如何在 Google 幻灯片中使用它们来提高工作效率。

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Google Workspace 专用 Gemini 是一个插件，可在 Google Workspace 中为客户提供生成式 AI 功能。在本迷你课程中，您将了解 Gemini 的主要功能，以及如何在 Google 表格中使用它们来提高工作效率。

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Google Workspace 专用 Gemini 是一个插件，用户可通过它来使用生成式 AI 功能。本课程通过视频课程、实操活动和实际示例，深入探讨了“Google 文档中的 Gemini”的功能。您将学习如何使用 Gemini 来根据提示生成书面内容。您还会探索如何使用 Gemini 来修改已撰写好的文本，帮助提升整体工作效率。学完本课程后，您将掌握相关知识和技能，能够自信地利用 Google 文档中的 Gemini 来提升写作水平。

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Google Workspace 专用 Gemini 是一个插件，可在 Google Workspace 中为客户提供生成式 AI 功能。在本迷你课程中，您将了解 Gemini 的主要功能，以及如何在 Gmail 中使用这些功能来提高工作效率。

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Google Workspace 专用 Gemini 是一个插件，可在 Google Workspace 中为客户提供生成式 AI 功能。在本学习路线中，您将了解 Gemini 的主要功能，以及如何在 Google Workspace 中使用它们来提高工作效率。

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Outline the key steps in publishing an API to deliver selective company information to applications created by external developers.

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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.

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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.

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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。

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本课程教您如何使用深度学习来创建图片标注模型。您将了解图片标注模型的不同组成部分，例如编码器和解码器，以及如何训练和评估模型。学完本课程，您将能够自行创建图片标注模型并用来生成图片说明。

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本课程向您介绍 Transformer 架构和 Bidirectional Encoder Representations from Transformers (BERT) 模型。您将了解 Transformer 架构的主要组成部分，例如自注意力机制，以及该架构如何用于构建 BERT 模型。您还将了解可以使用 BERT 的不同任务，例如文本分类、问答和自然语言推理。完成本课程估计需要大约 45 分钟。

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本课程简要介绍了编码器-解码器架构，这是一种功能强大且常见的机器学习架构，适用于机器翻译、文本摘要和问答等 sequence-to-sequence 任务。您将了解编码器-解码器架构的主要组成部分，以及如何训练和部署这些模型。在相应的实验演示中，您将在 TensorFlow 中从头编写简单的编码器-解码器架构实现代码，以用于诗歌生成。

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本课程将向您介绍注意力机制，这是一种强大的技术，可令神经网络专注于输入序列的特定部分。您将了解注意力的工作原理，以及如何使用它来提高各种机器学习任务的性能，包括机器翻译、文本摘要和问题解答。

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

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本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。

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随着企业对人工智能和机器学习的应用越来越广泛，以负责任的方式构建这些技术也变得更加重要。但对很多企业而言，真正践行 Responsible AI 并非易事。如果您有意了解如何在组织内践行 Responsible AI，本课程正适合您。 本课程将介绍 Google Cloud 目前如何践行 Responsible AI，以及从中总结的最佳实践和经验教训，便于您以此为框架构建自己的 Responsible AI 方法。

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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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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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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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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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完成开发 Google Cloud 网络课程，赢取技能徽章。在此课程中，您将学习 部署和监控应用的多种方法，包括执行以下任务的方法：探索 IAM 角色并添加/移除 项目访问权限、创建 VPC 网络、部署和监控 Compute Engine 虚拟机、 编写 SQL 查询、在 Compute Engine 中部署和监控虚拟机，以及使用 Kubernetes 通过多种部署方法部署应用。

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这是一套自助式速成课程，向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务。学员将通过一系列视频讲座、演示和实操实验，探索和部署各种解决方案元素，包括安全互连网络、负载均衡、自动扩缩、基础架构自动化和代管式服务。

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本课程指导学员运用久经考验的设计模式在 Google Cloud 上构建高度可靠且高效的解决方案。它是“Google Compute Engine 架构设计”或“Google Kubernetes Engine 架构设计”课程的延续，并假定您有使用其中任何一门课程所涵盖技术的实践经验。通过一系列演示、设计活动和动手实验，学员可以了解如何定义及平衡业务要求和技术要求，以便设计可靠性和可用性高、安全且经济实惠的 Google Cloud 部署。

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完成中级技能徽章课程使用 BigQuery 构建数据仓库，展示以下技能： 联接数据以创建新表、排查联接故障、使用并集附加数据、创建日期分区表， 以及在 BigQuery 中使用 JSON、数组和结构体。

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这门自助式速成课程向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务，其中着重介绍了 Compute Engine。学员将通过一系列视频讲座、演示和动手实验，探索和部署各种解决方案元素，包括网络、虚拟机和应用服务等基础架构组件。您将学习如何通过控制台和 Cloud Shell 使用 Google Cloud。您还将了解云架构师角色、基础架构设计方法以及虚拟网络配置和虚拟私有云 (VPC)、项目、网络、子网、IP 地址、路由及防火墙规则。

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这门自助式速成课程向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务，着重介绍了 Compute Engine。学员将通过一系列视频讲座、演示和动手实验，探索和部署各种解决方案元素，包括网络、系统和应用服务等基础架构组件。本课程的内容还包括如何部署实用的解决方案，包括客户提供的加密密钥、安全和访问权限管理、配额和结算，以及资源监控。

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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 Dataproc 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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This course helps learners create a study plan for the PCA (Professional Cloud Architect) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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

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

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

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完成入门级技能徽章课程为 Compute Engine 实现云负载均衡，展示以下方面的技能： 在 Compute Engine 中创建和部署虚拟机 以及配置网络和应用负载均衡器。

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完成“在 Google Cloud 上设置应用开发环境”课程，赢取技能徽章；通过该课程，您将了解如何使用以下技术的基本功能来构建和连接以存储为中心的云基础设施： Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。

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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.

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