Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions with Vertex AI course, where you will learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models. This skill badge course is for professional Data Scientists and Machine Learning Engineers. 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, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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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 participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.
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Earn the intermediate skill badge by completing the Classify Images with TensorFlow on Google Cloud course where you will learn how to use TensorFlow and Vertex AI to create and train machine learning models. You will primarily interact with Vertex AI Workbench user-managed notebooks. 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, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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本课程介绍 Google Cloud 中的 AI 和机器学习 (ML) 服务，这些服务可构建预测式和生成式 AI 项目。本课程探讨从数据到 AI 的整个生命周期中可用的技术、产品和工具，包括 AI 基础、开发和解决方案。通过引人入胜的学习体验和实操练习，本课程可帮助数据科学家、AI 开发者和机器学习工程师提升技能和知识水平。
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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model. 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.
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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。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可您在 Google Cloud 产品与服务方面的熟练度； 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。完成此技能徽章课程和作为最终评估的实验室挑战赛， 获得技能徽章，在您的人际圈中炫出自己的技能。
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Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.
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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML. 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 the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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完成中级技能徽章课程Create ML Models with BigQuery ML，展示您在以下方面的技能： 使用 BigQuery ML 构建和评估机器学习模型，以执行数据预测。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您在 Google Cloud 产品与服务方面的熟练度；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，即可获得技能徽章， 并在您的社交圈中秀一秀自己的能力。
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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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。 技能徽章是 Google Cloud 颁发的专属数字徽章， 旨在认可您在 Google Cloud 产品与服务方面的熟练度； 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。完成此技能徽章课程和作为最终评估的实验室挑战赛， 获得数字徽章，在您的人际圈中炫出自己的技能。
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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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完成入门级技能徽章课程“从 BigQuery 数据中挖掘数据洞见”，展示您在以下方面的技能： 编写 SQL 查询、查询公共表、将示例数据加载到 BigQuery 中、 在 BigQuery 中使用查询验证器排查常见的语法错误，以及通过连接到 BigQuery 数据在 Looker Studio 中 创建报告。 技能徽章是由 Google Cloud 颁发的专属数字徽章， 旨在认可您在 Google Cloud 产品与服务方面的熟练度。 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， 获得技能徽章，在您的人际圈中炫出自己的技能。
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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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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 Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
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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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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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Migration from MySQL to Cloud Spanner using Dataflow that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.
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Welcome Gamers! Test your skills and learn BigQuery, all while having fun!
You will compete to see who can finish the game with the highest score.
Earn the points by completing the steps in the lab.... and get bonus points
for speed!
Be sure to click "End" when you're done with each lab to get the maximum points.
All players will be awarded the game badge.
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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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Welcome Gamers! Today's game is all about experimenting with Big Query for Machine Learning! Use real life case studies to learn various concepts of BQML and have fun. Take labs to earn points. The faster you complete the lab objectives, the higher your score.
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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this advanced-level quest, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications. This quest will teach you the practical skills needed for integrating container orchestration into your own workflow. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, finish the additional Challenge Lab at the end of the Deploy to Kubernetes in Google Cloud Quest to receive an exclusive Google Cloud digital badge.
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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。
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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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。该课程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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Earn a skill badge by completing the Set Up a Google Cloud Network course, where you will learn how to: Set IAM features with the gcloud CLI tool; deploy and scale a web app on Google Compute Engine; provision a Kubernetes cluster and break an application into microservices; perform basic networking tasks on Google Cloud Platform; view BigQuery logs inside Cloud Logging, and migrate a stand-alone PostgreSQL database to Cloud SQL using a continuous Database Migration Service job. 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 the skill badge, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud.
Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
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This on-demand course equips students to understand and adopt Istio-based
service-mesh with Anthos for centralized observability, traffic management,
and service-level security.
This is the second course of the Architecting Hybrid Cloud Infrastructure with
Anthos series. After completing this course, learners should continue to the
Hybrid Cloud Multi-Cluster with Anthos course.
Completion of the Architecting with Google Kubernetes Engine path is a
prerequisite for this course.
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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.
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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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完成 Google Cloud 架构： 设计、实施及管理课程，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。 技能徽章 是由 Google Cloud 颁发的专属数字徽章，旨在认可 您在 Google Cloud 产品与服务方面的熟练度；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。完成此技能 徽章课程和作为最终评估的实验室挑战赛，即可获得技能徽章， 在您的人际圈中炫出自己的技能。
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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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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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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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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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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences.
“Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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There's much excitement about cloud technology and digital transformation, but often many unanswered questions.
For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin?
If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.
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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, Cloud 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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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge to demonstrate skills in the following: configuring and building Docker container images, creating and managing Google Kubernetes Engine (GKE) clusters, utilizing kubectl for efficient cluster management, and deploying Kubernetes applications with robust continuous delivery (CD) practices. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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Earn a skill badge by completing the Develop your Google Cloud Network course, where you learn multiple ways to deploy and monitor applications including how to: explore IAM rols and add/remove project access, create VPC networks, deploy and monitor Compute Engine VMs, write SQL queries, deploy and monitor VMs in Compute Engine, and deploy applications using Kubernetes with multiple deployment approaches. A skill badge is an exclusivedigital 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, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
Earn a skill badge by completing the Build and Secure Networks in Google Cloud course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud. 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 the skill badge, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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完成Implement Cloud Security Fundamentals on Google Cloud技能徽章中级课程， 展示自己在以下方面的技能：使用 Identity and Access Management (IAM) 创建和分配角色； 创建和管理服务账号；跨虚拟私有云 (VPC) 网络实现专用连接； 使用 Identity-Aware Proxy 限制应用访问权限； 使用 Cloud Key Management Service (KMS) 管理密钥和加密数据；创建专用 Kubernetes 集群。 技能徽章是由 Google Cloud 颁发的专有数字徽章，旨在认可 您在 Google Cloud 产品与服务方面的熟练度；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。完成此技能 徽章课程和作为最终评估的实验室挑战赛，即可获得技能徽章， 并在您的社交圈中秀一秀自己的能力。
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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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Service level indicators (SLIs) and service level objectives (SLOs) are fundamental tools for measuring and managing reliability. In this course, students learn approaches for devising appropriate SLIs and SLOs and managing reliability through the use of an error budget.
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This self-paced training course gives participants broad study of networking options on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy Google Cloud networking technologies, such as Google Cloud Virtual Private Cloud (VPC) networks, subnets and firewalls. The course also covers access control to networks, sharing networks and load balancing.
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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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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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Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.
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For everyone using Google Cloud Platform for the first time, getting familar with gcloud, Google Cloud's command line, will help you get up to speed faster. In this quest, you'll learn how to install and configure Cloud SDK, then use gcloud to perform some basic operations like creating VMs, networks, using BigQuery, and using gsutil to perform operations.
Build and Deploy Machine Learning Solutions on Vertex AI
Earned May 16, 2024 EDT
Generative AI Explorer - Vertex AI
Earned May 15, 2024 EDT
Production Machine Learning Systems
Earned May 6, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned May 6, 2024 EDT
Machine Learning in the Enterprise
Earned May 5, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned May 5, 2024 EDT
Classify Images with TensorFlow on Google Cloud
Earned Mar 26, 2024 EDT
TensorFlow on Google Cloud
Earned Mar 25, 2024 EDT
Launching into Machine Learning
Earned Mar 22, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud - 简体中文
Earned Mar 22, 2024 EDT
BigQuery for Machine Learning
Earned Mar 14, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned Dec 4, 2023 EST
Data Lake Modernization on Google Cloud: Cloud Composer
Earned Dec 4, 2023 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned Dec 4, 2023 EST
Create ML Models with BigQuery ML
Earned Dec 4, 2023 EST
Building Batch Data Pipelines on Google Cloud
Earned Dec 2, 2023 EST
Build a Data Warehouse with BigQuery
Earned Oct 23, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Oct 23, 2023 EDT
Derive Insights from BigQuery Data
Earned Oct 23, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Oct 21, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Oct 18, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Oct 17, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Oct 16, 2023 EDT
MySQL to Cloud Spanner
Earned Jun 26, 2023 EDT
BigQuery for Data Analysis II
Earned Jun 23, 2023 EDT
Introduction to Generative AI Studio - 简体中文
Earned Jun 22, 2023 EDT
Intro to BigQuery: Analytics & Machine Learning
Earned Jun 20, 2023 EDT
Kubernetes in Google Cloud
Earned Jun 19, 2023 EDT
Generative AI Fundamentals - 简体中文
Earned Jun 13, 2023 EDT
Create Image Captioning Models - 简体中文
Earned Jun 13, 2023 EDT
Transformer Models and BERT Model - 简体中文
Earned Jun 13, 2023 EDT
Encoder-Decoder Architecture - 简体中文
Earned Jun 3, 2023 EDT
Attention Mechanism - 简体中文
Earned Jun 3, 2023 EDT
Introduction to Image Generation - 简体中文
Earned Jun 3, 2023 EDT
Introduction to Responsible AI - 简体中文
Earned Jun 3, 2023 EDT
Introduction to Large Language Models - 简体中文
Earned Jun 3, 2023 EDT
Introduction to Generative AI - 简体中文
Earned Jun 3, 2023 EDT
Set Up a Google Cloud Network
Earned May 29, 2023 EDT
Optimize Costs for Google Kubernetes Engine
Earned May 29, 2023 EDT
Logging and Monitoring in Google Cloud
Earned May 16, 2023 EDT
Hybrid Cloud Service Mesh with Anthos
Earned May 12, 2023 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned May 12, 2023 EDT
Preparing for your Professional Cloud Architect Journey
Earned Apr 26, 2023 EDT
Cloud Architecture: Design, Implement, and Manage
Earned Apr 25, 2023 EDT
Cloud Architecture
Earned Apr 20, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process - 简体中文
Earned Mar 25, 2023 EDT
Scaling with Google Cloud Operations
Earned Dec 12, 2022 EST
Modernize Infrastructure and Applications with Google Cloud
Earned Dec 12, 2022 EST
Exploring Data Transformation with Google Cloud
Earned Dec 12, 2022 EST
Digital Transformation with Google Cloud
Earned Dec 12, 2022 EST
Managing Security in Google Cloud
Earned Dec 6, 2022 EST
Deploy Kubernetes Applications on Google Cloud
Earned Dec 3, 2022 EST
Build Infrastructure with Terraform on Google Cloud
Earned Dec 3, 2022 EST
Develop your Google Cloud Network
Earned Dec 3, 2022 EST
Implement Load Balancing on Compute Engine
Earned Dec 1, 2022 EST
Build a Secure Google Cloud Network
Earned Dec 1, 2022 EST
Implement Cloud Security Fundamentals on Google Cloud
Earned Nov 30, 2022 EST
Mitigating Security Vulnerabilities on Google Cloud
Earned Nov 30, 2022 EST
Site Reliability Engineering: Measuring and Managing Reliability
Earned Nov 29, 2022 EST
Networking in Google Cloud: Defining and Implementing Networks
Earned Nov 9, 2022 EST
Elastic Google Cloud Infrastructure: Scaling and Automation - 简体中文
Earned Nov 5, 2022 EDT
Preparing for Your Associate Cloud Engineer Journey
Earned Nov 4, 2022 EDT
Exploring APIs
Earned Nov 3, 2022 EDT
Using the Cloud SDK Command Line
Earned Nov 3, 2022 EDT
基准：基础架构
Earned Nov 3, 2022 EDT
Google Cloud Essentials
Earned Nov 3, 2022 EDT
Essential Google Cloud Infrastructure: Core Services - 简体中文
Earned Nov 1, 2022 EDT
Architecting with Google Kubernetes Engine: Foundations - 简体中文
Earned Oct 31, 2022 EDT
Essential Google Cloud Infrastructure: Foundation - 简体中文
Earned Sep 15, 2022 EDT
Google Cloud Fundamentals: Core Infrastructure - 简体中文
Earned Sep 10, 2022 EDT