This instructor-led course provides an in-depth understanding of managing scalable workloads using Google Kubernetes Engine (GKE). Participants will explore advanced Kubernetes concepts including multi-cluster architectures, GKE fleets, Cloud Service Mesh, fleet networking, centralized configuration management, workload identity, security posture management, and CI/CD automation. 

Through hands-on labs and real-world implementation scenarios, learners will gain practical experience deploying, managing, observing, and securing applications across distributed Kubernetes environments using Google Cloud-native services and modern DevOps practices. The course also covers scalable networking, traffic management, GitOps-based configuration management, compliance controls, and AI/ML workload deployment on Kubernetes infrastructure.

After completing GKE, students will be able to:

  • Design and manage multi-cluster GKE architectures.
  • Understand and implement GKE fleets and fleet management.
  • Configure centralized Kubernetes configuration management using GitOps.
  • Implement fleet networking and multi-cluster communication
  • Deploy and manage Cloud Service Mesh.
  • Configure advanced traffic management and routing strategies.
  • Secure microservices using authentication, authorization, and mTLS.
  • Implement workload identity and identity federation in GKE.
  • Apply security posture and compliance best practices.
  • Build secure CI/CD pipelines for Kubernetes workloads
  • Deploy and optimize AI and ML workloads on GKE.
  • Implement observability and operational best practices for Kubernetes environments.

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Key Features of GKE

  • Advanced Kubernetes and GKE Architecture

    • Multi-cluster Kubernetes architecture design
    • GKE fleet management concepts
    • Distributed Kubernetes administration
    • Scalable application deployment strategies 

  • Hands-On Learning Experience 

    • Real-world Kubernetes implementation labs
    • Guided deployment and troubleshooting exercises
    • Enterprise-grade Kubernetes use cases
    • Practical DevOps and GitOps workflows 

     

  • Multi-Cluster and Fleet Management

    • GKE fleet concepts and implementation
    • Centralized cluster management
    • Cross-cluster communication and connectivity
    • Multi-cluster workload orchestration 

  • GitOps and Configuration Management

    • Config Sync and Policy Controller
    • Infrastructure as Code concepts
    • Kubernetes configuration automation
    • Config Connector and Blueprints 

  •  Cloud Service Mesh and Networking

    • Cloud Service Mesh architecture and deployment
    • Service-to-service communication
    • Advanced traffic routing and resiliency
    • East-west and north-south traffic management 

  • Kubernetes Security and Identity

    • Authentication and authorization in GKE
    • Workload Identity and identity federation
    • mTLS implementation and secure communication
    • Security posture and compliance controls 

  •  CI/CD and DevOps Automation

    • Cloud Build and Cloud Deploy integration
    • Secure software supply chain practices
    • Automated deployment pipelines
    • Progressive delivery strategies 

  • AI and ML Workloads on Kubernetes

    • Running AI/ML workloads on GKE
    • Model training and serving architectures
    • GPU and scalable workload optimization
    • Cost optimization strategies for AI workloads 

Who should Attend Google Kubernetes Engine (GKE) Course?

  • Cloud Engineers
  • DevOps Engineers
  • Platform Engineers
  • Kubernetes Administrators
  • Cloud Architects
  • Site Reliability Engineers (SREs)
  • Infrastructure Engineers
  • AI/ML Infrastructure Engineers
  • Professionals managing scalable Kubernetes environments

Prerequisites of Google Kubernetes Engine

  • Google Cloud Fundamentals or equivalent cloud experience
  • Prior experience with Kubernetes concepts and administration (recommended)
  • Familiarity with containerized applications and Docker
  • Basic understanding of networking and DevOps concepts
  • Understanding of CI/CD fundamentals is beneficial

    • Learning Objective of Google Kubernetes Engine

    • Understand scalable Kubernetes and GKE architecture concepts
    • Design and manage multi-cluster GKE environments
    • Implement GKE fleets and centralized cluster management
    • Configure GitOps workflows using Config Sync and Policy Controller
    • Deploy and manage Cloud Service Mesh
    • Implement advanced networking and traffic management strategies
    • Secure Kubernetes workloads using mTLS and identity management
    • Apply compliance and security posture best practices
    • Build CI/CD pipelines for Kubernetes applications
    • Deploy and optimize AI and ML workloads on GKE
    • Apply operational best practices for scalable Kubernetes infrastructure

    Why choose CloudThat as your training partner?

    • Specialized Google Cloud and Kubernetes Expertise  -CloudThat specializes in cloud-native technologies, Kubernetes, DevOps, and Google Cloud solutions with a strong focus on enterprise implementation practices. 
    • Industry-Recognized Trainers -Our trainers are certified Google Cloud and Kubernetes experts with practical experience in scalable cloud-native architectures and production-grade Kubernetes deployments. 
    • Hands-On Learning Approach - CloudThat emphasizes practical learning through labs, real-world scenarios, troubleshooting exercises, and implementation-focused demonstrations. 
    • Customized Learning Paths - Training paths are designed for DevOps engineers, cloud architects, Kubernetes administrators, and platform engineering teams with varying experience levels. 
    • Interactive and Enterprise-Focused Sessions - Sessions include architecture discussions, deployment walkthroughs, live demonstrations, and operational best practices.
    • Career and Certification Support - CloudThat supports learners with project guidance, interview preparation, and cloud-native career learning paths. 
    • Updated Industry-Relevant Content - Course materials are continuously updated to align with the latest GKE, Kubernetes, service mesh, CI/CD, and AI infrastructure advancements.
    • Trusted by Global Enterprises - Thousands of professionals and enterprise customers trust CloudThat for advanced cloud-native and Kubernetes training programs. 

    Course Outline for GKE Download Course Outline

    Lecture Content

    • Multi-Cluster Kubernetes Overview
    • Introduction to GKE Fleets
    • Sameness and Trust Concepts
    • Fleet Management Architecture
    • Scaling Kubernetes Environments

    Lecture Content

    • Centralized Cluster Management
    • Multi-Cluster Architecture Design
    • Cluster Connectivity and Access Patterns
    • Hybrid and Distributed Kubernetes Architectures
    • Operational Considerations for Large-Scale Clusters

    Lecture Content

    • Deep Dive into GKE Fleets
    • Fleet Management Use Cases
    • Team and Resource Isolation Strategies
    • Managing Shared Kubernetes Infrastructure
    • Fleet Governance Concepts

    Lab Content

    • Managing Workloads at Scale with GKE Fleets

    Lecture Content

    • Configuration Management Challenges
    • GitOps Concepts and Workflows
    • Config Sync Architecture
    • Policy Controller and Governance
    • Config Connector and Blueprints
    • Infrastructure Standardization Strategies

    Lab Content

    • Automating Configuration with Config Sync

    Lecture Content

    • Fleet Networking Architectur
    • Pod-to-Pod Communication
    • Multi-Cluster Services
    • Multi-Cluster Gateway Concepts
    • Kubernetes Networking Best Practices

    Lab Content

    • Deploying Multi-Cluster Gateway

    Lecture Content

    • Introduction to Cloud Service Mesh
    • Service Mesh Architecture and ComponentsArchitecture
    • Service Discovery Concepts
    • Observability within Service Mesh
    • Benefits of Service Mesh in Kubernetes

    Lab Content

    • Installing Cloud Service Mesh

    Lecture Content

    • VirtualService and DestinationRule
    • Gateway Configuration and Ingress
    • Traffic Routing and Load Balancing
    • Traffic Resilience and Fault Tolerance
    • Canary and Progressive Deployment Concepts

    Lab Content

    • Managing Traffic Flow

    Lecture Content

    • Authentication and Authorization Concepts
    • Mutual TLS (mTLS) Encryption
    • Access Control Policies
    • Zero Trust Security Concepts
    • Secure Microservices Communication

    Lab Content

    • Securing Service Mesh

    Lecture Content

    • East-West Traffic Routing
    • Multi-Network Communication
    • Cross-Cluster Connectivity
    • Distributed Service Communication
    • Hybrid Networking Considerations

    Lab Content

    • Managing Distributed Services

    Lecture Content

    • GKE Identity Service
    • Connect Gateway Architecture
    • Third-Party Identity Providers
    • Workload Identity Concepts
    • Identity Federation Strategies

    Lab Content

    • Managing Authentication at Scale

    Lecture Content

    • Security Posture Dashboard
    • Node Security Best Practices
    • Vulnerability Scanning Concepts
    • Security Command Center Integration
    • Compliance and Governance Controls

    Lecture Content

    • Cloud Build and Cloud Deploy
    • CI/CD Pipeline Architectures
    • Deployment Strategies and Automation
    • Private CI/CD Pipelines
    • Software Supply Chain Security
    • DevSecOps Best Practices

    Lab Content

    • Creating CI/CD Pipelines

    Lecture Content

    • AI and ML Workloads on Kubernetes
    • Model Training and Distributed Computing
    • Model Serving Architectures
    • GPU and Resource Optimization
    • Cost Optimization Strategies
    • Scalable AI Infrastructure Patterns

    Certification Details of GKE

      Course Completion Certificate

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    Course ID: 28315

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    FAQs for Google Kubernetes Engine Course

    This course is designed for cloud engineers, DevOps professionals, Kubernetes administrators, architects, and platform engineers working with scalable Kubernetes environments.

    The course covers GKE fleets, multi-cluster architecture, service mesh, networking, security, identity management, CI/CD, GitOps, and AI workloads on Kubernetes.

    Yes, basic to intermediate Kubernetes knowledge is recommended.

    The course duration is approximately 3 days instructor-led training.

    Yes, the course includes multiple hands-on labs covering real-world Kubernetes and GKE implementation scenarios.

    Yes, the course covers authentication, authorization, mTLS, workload identity, compliance, and security posture management.

    Yes, the course includes Cloud Build, Cloud Deploy, supply chain security, and automated deployment pipelines.

    Yes, learners will deploy and manage Cloud Service Mesh, traffic routing, and secure microservices communication.

    Yes, the course includes deployment and optimization of AI/ML workloads on GKE.

    The course includes GKE, GKE Fleets, Cloud Service Mesh, Config Sync, Policy Controller, Cloud Build, Cloud Deploy, Security Command Center, and workload identity services.

    Enquire Now