Optimize Costs for Google Kubernetes Engine
Earned juil. 5, 2023 EDT
Set Up a Google Cloud Network
Earned juil. 5, 2023 EDT
Build Infrastructure with Terraform on Google Cloud
Earned juil. 2, 2023 EDT
Develop Your Google Cloud Network
Earned juin 29, 2023 EDT
Prepare Data for ML APIs on Google Cloud
Earned juin 26, 2023 EDT
Logging and Monitoring in Google Cloud
Earned juin 18, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned juin 15, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned juin 14, 2023 EDT
Preparing for your Professional Cloud Architect Journey
Earned mai 23, 2023 EDT
Set Up an App Dev Environment on Google Cloud
Earned mai 9, 2023 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned mai 8, 2023 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned mai 4, 2023 EDT
Essential Google Cloud Infrastructure: Core Services
Earned mai 1, 2023 EDT
Essential Google Cloud Infrastructure: Foundation
Earned avr. 28, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned avr. 24, 2023 EDT
Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.
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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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This on-demand course provides partners the skills required to design, deploy, and monitor Vertail AI Search for Commerce solutions including retail search and recommendation AI for enterprise customers.
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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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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 learn about the various services Google Cloud offers for modernizing retail applications and infrastructure. Through a series of lecture content and hands-on labs, you will gain practical experience deploying cutting-edge retail and ecommerce solutions on Google Cloud.
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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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Earn a skill badge by completing the Cloud Architecture: Design, Implement, and Manage to demonstrate skills in the following: deploy a publicly accessible website using Apache web servers, configure a Compute Engine VM using startup scripts, configure secure RDP using a Windows Bastion host and firewall rules, build and deploy a Docker image to a Kubernetes cluster and then update it, and create a CloudSQL instance and import a MySQL database. This skill badge is a great resource for understanding topics that will appear in the Google Cloud Certified Professional Cloud Architect certification exam.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge course 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.
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Earn a skill badge by completing the Set Up a Google Cloud Network skill badge 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.
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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.
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Earn a skill badge by completing the Develop your Google Cloud Network skill badge course, where you learn multiple ways to deploy and monitor applications including how to: explore IAM roles 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.
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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.
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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 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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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud skill badge course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.
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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.
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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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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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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.
Prompt Design in Vertex AI
Earned juil. 9, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned juil. 1, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned juin 30, 2025 EDT
Vertex AI Search for Commerce
Earned juin 30, 2025 EDT
Serverless Data Processing with Dataflow: Foundations
Earned juin 26, 2025 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned juin 26, 2025 EDT
Modernizing Retail and Ecommerce Solutions with Google Cloud
Earned juin 12, 2025 EDT
Introduction to Large Language Models
Earned mai 23, 2025 EDT
Introduction to Generative AI
Earned mai 23, 2025 EDT
Cloud Architecture: Design, Implement, and Manage
Earned juil. 10, 2023 EDT
Optimize Costs for Google Kubernetes Engine
Earned juil. 5, 2023 EDT
Set Up a Google Cloud Network
Earned juil. 5, 2023 EDT
Build Infrastructure with Terraform on Google Cloud
Earned juil. 2, 2023 EDT
Develop Your Google Cloud Network
Earned juin 29, 2023 EDT
Prepare Data for ML APIs on Google Cloud
Earned juin 26, 2023 EDT
Logging and Monitoring in Google Cloud
Earned juin 18, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned juin 15, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned juin 14, 2023 EDT
Preparing for your Professional Cloud Architect Journey
Earned mai 23, 2023 EDT
Set Up an App Dev Environment on Google Cloud
Earned mai 9, 2023 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned mai 8, 2023 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned mai 4, 2023 EDT
Essential Google Cloud Infrastructure: Core Services
Earned mai 1, 2023 EDT
Essential Google Cloud Infrastructure: Foundation
Earned avr. 28, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned avr. 24, 2023 EDT