Cloud Operations and Service Mesh with Anthos
Earned Apr 3, 2023 EDT
Developing a Google SRE Culture
Earned Mar 30, 2023 EDT
Develop your Google Cloud Network
Earned Mar 23, 2023 EDT
Set Up an App Dev Environment on Google Cloud
Earned Mar 22, 2023 EDT
Implement Load Balancing on Compute Engine
Earned Mar 22, 2023 EDT
Build Infrastructure with Terraform on Google Cloud
Earned Mar 22, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned Mar 21, 2023 EDT
Preparing for Your Associate Cloud Engineer Journey
Earned Mar 21, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Mar 15, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned Mar 3, 2023 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned Feb 27, 2023 EST
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned Feb 27, 2023 EST
Essential Google Cloud Infrastructure: Core Services
Earned Feb 26, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned Feb 25, 2023 EST
Preparing for your Professional Cloud Architect Journey
Earned Feb 8, 2023 EST
Essential Google Cloud Infrastructure: Foundation
Earned May 20, 2022 EDT
Scaling with Google Cloud Operations
Earned May 18, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned May 18, 2022 EDT
Exploring Data Transformation with Google Cloud
Earned May 18, 2022 EDT
Digital Transformation with Google Cloud
Earned May 18, 2022 EDT
Google Cloud Essentials
Earned May 26, 2019 EDT
This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.
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This course introduces Vertex AI Studio, a tool for prototyping and customizing generative AI models. Through immersive lessons, engaging demos, and a hands-on lab, you'll explore the generative AI workflow and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, and model tuning. The aim is to enable you to unlock the potentials of these models in your projects with Vertex AI Studio.
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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. 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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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles.
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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
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In this course you will learn the fundamentals of no-code app development and recognize use cases for no-code apps. The course provides an overview of the AppSheet no-code app development platform and its capabilities. You learn how to create an app with data from spreadsheets, create the app’s user experience using AppSheet views and publish the app to end users.
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Google Workspace Mail Management is the fourth course in the Google Workspace Administration series.
In this course you will learn how to protect your organization against spam, spoofing, phishing and malware attacks. You will configure email compliance and learn how to implement data loss prevention (DLP) for your organization. You will gain an understanding of the mail routing options available and learn how to allowlist and block senders. You will also become familiar with other mail options such as inbound and outbound gateways, 3rd party email archiving, and journaling to Vault.
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Google Workspace Security is the third course in the Google Workspace Administration series.
In this course you will focus on the various aspects of Google Workspace Security including user password policies and how to enable and enforce two step verification (2SV) for your users. You will learn about application security and understand how to whitelist and block API access to your account. You will see how Google Workspace can easily be integrated with a number of predefined 3rd party applications. You will also become familiar with the SSO options in Google Workspace. Finally you will understand how to spot potential security risks within your organization and learn how to address them using the tools available in the admin console.
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Managing Google Workspace is the second course in the Google Workspace Administration series.
This course focuses on the Google Workspace core services such as Gmail, Calendar, and Drive & Docs. You will become familiar with the various service settings, and learn how to enable them for all or just a subset of your users. You will gain an understanding of Google Vault, Google’s ediscovery service. You will understand the various admin console reports that are available and be able to search and filter the information in these reports. Finally you will see how multiple domains can be used with Google Workspace and learn how to add a new domain to your account.
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Planning for a Google Workspace Deployment is the final course in the Google Workspace Administration series.
In this course, you will be introduced to Google's deployment methodology and best practices. You will follow Katelyn and Marcus as they plan for a Google Workspace deployment at Cymbal. They'll focus on the core technical project areas of provisioning, mail flow, data migration, and coexistence, and will consider the best deployment strategy for each area.
You will also be introduced to the importance of Change Management in a Google Workspace deployment, ensuring that users make a smooth transition to Google Workspace and gain the benefits of work transformation through communications, support, and training.
This course covers theoretical topics, and does not have any hands on exercises. If you haven’t already done so, please cancel your Google Workspace trial now to avoid any unwanted charges.
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Introduction to Google Workspace Administration is the first course in the Google Workspace Administration series of courses.
This series will serve as the starting place for any new Google Workspace admin as they begin their journey of managing and establishing Google Workspace best practices for their organization. These courses together will leave you feeling confident to utilize the basic functions of the admin console to manage users, control access to services, configure security settings, and much more.
Through a series of readings and step-by-step hands-on exercises, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as Google Workspace administrators.
In this course you will sign up for a Google Workspace account and configure your DNS records for Google Workspace. You will learn how to provision and manage your users, and will create groups and calendar resources for your organization. You will be introduced to …
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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 explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This third course covers cloud automation and management tools and building secure networks.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This first course provides an overview of cloud computing, ways to use Google Cloud, and different compute options.
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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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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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Earn a skill badge by completing the Set Up a Google Cloud Network course, where you will learn how to perform basic networking tasks on Google Cloud Platform - create a custom network, add subnets firewall rules, then create VMs and test the latency when they communicate with each other. 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 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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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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Complete the intermediate Build a Data Warehouse with BigQuery skill badge to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery. 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. For practice with BigQuery fundamentals (including working with the console and command line), complete the course titled BigQuery Basics for Data Analysts.
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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API. 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 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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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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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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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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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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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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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.
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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 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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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 Implement Cloud Security Fundamentals on Google Cloud skill badge to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster. 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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If you want to take your Google Cloud networking skills to the next level, look no further. This course is composed of labs that cover real-life use cases and it will teach you best practices for overcoming common networking bottlenecks. From getting hands-on practice with testing and improving network performance, to integrating high-throughput VPNs and networking tiers, Network Performance and Optimization is an essential course for Google Cloud developers who are looking to double down on application speed and robustness.
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This course helps you structure your preparation for the
Professional 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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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This quest will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Networking Fundamentals in the Google Cloud will give you the practical experience needed so you can start building robust networks right away.
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Welcome to the second course in the networking and Google Cloud series routing and addressing.
In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities.
Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses.
By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.
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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.
This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques.
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Course four of the Anthos series prepares students to consider multiple approaches for modernizing applications and services within Anthos environments. Topics include optimizing workloads on serverless platforms and migrating workloads to Anthos. This course is a continuation of course three, Anthos on Bare Metal, and assumes direct experience with the topics covered in that course.
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This course introduces you to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to on Google Cloud using Cloud Run.design, implement, deploy, secure, manage, and scale applications
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to develop more secure applications, implement federated identity management, and integrate application components by using messaging, event-driven processing, and API gateways. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer. This is the second course of the Developing Applications with Google Cloud series. After completing this course, enroll in the App Deployment, Debugging, and Performance course.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to apply best practices for application development and use the appropriate Google Cloud storage services for object storage, relational data, caching, and analytics.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
This is the first course of the Developing Applications with Google Cloud series. After completing this course, enroll in the Securing and Integrating Components of your Application course.
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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services.
This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
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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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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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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.
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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub. 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 skill badge that you can share with your network.
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Complete the introductory Implement Load Balancing on Compute Engine skill badge to demonstrate skills in the following: writing gcloud commands and using Cloud Shell, creating and deploying virtual machines in Compute Engine, and configuring network and HTTP load balancers. 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 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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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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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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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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Welcome to the Getting Started with Google Kubernetes Engine course. If you're interested in Kubernetes, a software layer that sits between your applications and your hardware infrastructure, then you’re in the right place! Google Kubernetes Engine brings you Kubernetes as a managed service on Google Cloud.
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud. The course starts with a basic introduction to Google Cloud, and is then followed by an overview of containers and Kubernetes, Kubernetes architecture, and Kubernetes operations.
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This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
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Google Cloud Fundamentals: Core Infrastructure 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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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 accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, virtual machines and applications services. You will learn how to use the Google Cloud through the console and Cloud Shell. You'll also learn about the role of a cloud architect, approaches to infrastructure design, and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules.
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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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In this introductory-level Quest, you will get hands-on practice with the Google Cloud’s fundamental tools and services. Google Cloud Essentials is the recommended first Quest for the Google Cloud learner - you will come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features. 1-minute videos walk you through key concepts for each lab.
Introduction to AI and Machine Learning on Google Cloud
Earned Oct 15, 2023 EDT
Security & Identity Fundamentals
Earned Jun 27, 2023 EDT
Introduction to Vertex AI Studio
Earned Jun 24, 2023 EDT
Create Image Captioning Models
Earned Jun 24, 2023 EDT
Transformer Models and BERT Model
Earned Jun 24, 2023 EDT
Attention Mechanism
Earned Jun 24, 2023 EDT
Encoder-Decoder Architecture
Earned Jun 24, 2023 EDT
Introduction to Image Generation
Earned Jun 24, 2023 EDT
Generative AI Fundamentals
Earned Jun 22, 2023 EDT
Introduction to Responsible AI
Earned Jun 22, 2023 EDT
Introduction to Large Language Models
Earned Jun 22, 2023 EDT
Introduction to Generative AI
Earned Jun 22, 2023 EDT
Building No-Code Apps with AppSheet: Foundations
Earned Jun 22, 2023 EDT
Google Workspace Mail Management
Earned Jun 17, 2023 EDT
Google Workspace Security
Earned Jun 17, 2023 EDT
Managing Google Workspace
Earned Jun 17, 2023 EDT
Planning for a Google Workspace Deployment
Earned Jun 16, 2023 EDT
Introduction to Google Workspace Administration
Earned Jun 15, 2023 EDT
Launching into Machine Learning
Earned Jun 12, 2023 EDT
How Google Does Machine Learning
Earned Jun 8, 2023 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned Jun 7, 2023 EDT
Google Cloud Computing Foundations: Networking & Security in Google Cloud
Earned Jun 7, 2023 EDT
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Earned Jun 7, 2023 EDT
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Earned Jun 5, 2023 EDT
Optimize Costs for Google Kubernetes Engine
Earned Jun 5, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Jun 5, 2023 EDT
Set Up a Google Cloud Network
Earned Jun 3, 2023 EDT
Deploy Kubernetes Applications on Google Cloud
Earned Jun 3, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 2, 2023 EDT
Build a Data Warehouse with BigQuery
Earned May 29, 2023 EDT
Prepare Data for ML APIs on Google Cloud
Earned May 26, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 24, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 23, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned May 20, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned May 19, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned May 17, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 17, 2023 EDT
Build a Secure Google Cloud Network
Earned May 10, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned May 10, 2023 EDT
Mitigating Security Vulnerabilities on Google Cloud
Earned May 9, 2023 EDT
Security Best Practices in Google Cloud
Earned May 5, 2023 EDT
Managing Security in Google Cloud
Earned Apr 29, 2023 EDT
Implement Cloud Security Fundamentals on Google Cloud
Earned Apr 26, 2023 EDT
Network Performance and Optimization
Earned Apr 25, 2023 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned Apr 24, 2023 EDT
Networking Fundamentals in Google Cloud
Earned Apr 24, 2023 EDT
Networking in Google Cloud: Routing and Addressing
Earned Apr 24, 2023 EDT
Networking in Google Cloud: Fundamentals
Earned Apr 23, 2023 EDT
Hybrid Cloud Modernizing Applications with Anthos
Earned Apr 17, 2023 EDT
Application Development with Cloud Run
Earned Apr 17, 2023 EDT
App Deployment, Debugging, and Performance
Earned Apr 16, 2023 EDT
Securing and Integrating Components of your Application
Earned Apr 16, 2023 EDT
Getting Started With Application Development
Earned Apr 15, 2023 EDT
Enterprise Database Migration
Earned Apr 7, 2023 EDT
Cloud Operations and Service Mesh with Anthos
Earned Apr 3, 2023 EDT
Developing a Google SRE Culture
Earned Mar 30, 2023 EDT
Develop your Google Cloud Network
Earned Mar 23, 2023 EDT
Set Up an App Dev Environment on Google Cloud
Earned Mar 22, 2023 EDT
Implement Load Balancing on Compute Engine
Earned Mar 22, 2023 EDT
Build Infrastructure with Terraform on Google Cloud
Earned Mar 22, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned Mar 21, 2023 EDT
Preparing for Your Associate Cloud Engineer Journey
Earned Mar 21, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Mar 15, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned Mar 3, 2023 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned Feb 27, 2023 EST
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned Feb 27, 2023 EST
Essential Google Cloud Infrastructure: Core Services
Earned Feb 26, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned Feb 25, 2023 EST
Preparing for your Professional Cloud Architect Journey
Earned Feb 8, 2023 EST
Essential Google Cloud Infrastructure: Foundation
Earned May 20, 2022 EDT
Scaling with Google Cloud Operations
Earned May 18, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned May 18, 2022 EDT
Exploring Data Transformation with Google Cloud
Earned May 18, 2022 EDT
Digital Transformation with Google Cloud
Earned May 18, 2022 EDT
Google Cloud Essentials
Earned May 26, 2019 EDT