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DevOps Training with GenAI Benefits
The DevOps market is growing rapidly, as organizations worldwide adopt DevOps practices driven by cloud-native technologies, containerization, and AI-powered automation. Our comprehensive program equips you with job-ready skills to stand out, as demand for skilled DevOps professionals is at an all-time high.
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Why Advanced DevOps Certification Training with GenAI from edureka
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- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
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- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
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- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Advanced DevOps Certification Training with GenAI
DevOps Skills Covered
DevOps Tools Covered
DevOps Course Curriculum
Curriculum Designed by Experts
Module 1: DevOps Fundamentals, Culture, and AI Transformation
9 Topics
Topics
- Introduction to DevOps: History, Evolution & Benefits
- DevOps Culture, Practices & Core Principles
- DLC vs DevOps Lifecycle
- DevOps Toolchain Overview & Tool Selection
- Agile, Lean & DevOps Integration
- Key Metrics: DORA Metrics, Lead Time, MTTR, Change Failure Rate
- DevOps Transformation Roadmap
- Introduction to AI in DevOps: Overview & Use Cases
- Essential Linux Commands for DevOps
![skill]( "Hands On Experience")
- Set up DevOps learning environment
- Create DevOps maturity assessment for a sample organization
- Design a DevOps transformation roadmap
- Calculate and analyze DORA metrics from case studies
- Practice essential Linux commands for daily DevOps tasks
- Explore AI tools landscape for DevOps
![skill]( "skill you will learn")
- Understanding DevOps philosophy, principles, and lifecycle
- Differentiating between traditional and DevOps approaches
- Identifying key DevOps metrics and KPIs (DORA)
- Recognizing AI's transformative role in DevOps
- Mapping DevOps career paths
Module 2: GitHub Copilot & AI-Assisted Development
9 Topics
Topics
- Introduction to GitHub Copilot & AI Coding Assistants
- Setting up Copilot in VS Code & JetBrains IDEs
- Copilot for Shell Script & Bash Generation
- AI-Assisted Python Scripting for Automation
- Prompt Engineering Best Practices for DevOps
- Copilot Chat for Code Explanation & Debugging
- AI Code Review & Suggestions
- Copilot for Documentation Generation
- Ethical AI Usage & Best Practices
![skill]( "Hands On Experience")
- Install and configure GitHub Copilot in VS Code
- Generate shell scripts for system automation using Copilot
- Create Python automation scripts with AI assistance
- Use Copilot Chat for code explanation and debugging
- Practice prompt engineering for DevOps tasks
- Generate documentation using Copilot
- Compare AI-generated code with manual coding
![skill]( "skill you will learn")
- Setting up and configuring GitHub Copilot
- Leveraging AI for code and script generation
- Using Copilot for infrastructure code generation
- Applying prompt engineering techniques for DevOps
- Accelerating development with AI assistance throughout course
Module 3: Version Control with Git & GitHub
10 Topics
Topics
- Git Fundamentals: Architecture & Workflow
- Git Installation & Configuration
- Repository Operations: Clone, Commit, Push, Pull
- Branching Strategies: GitFlow, Trunk-Based Development
- Merging, Rebasing & Conflict Resolution
- GitHub: Pull Requests, Code Reviews, Issues
- Git Tags, Stash & Advanced Commands
- Git Hooks & Pre-commit Automation
- GitHub Actions Introduction
- Using Copilot for Git Commands & Workflows
![skill]( "Hands On Experience")
- Initialize Git repository for a project
- Create feature branches and merge workflows
- Resolve merge conflicts in team scenarios
- Set up GitHub repository with branch protection
- Implement GitFlow workflow with Copilot assistance
- Create and manage pull requests with code reviews
- Configure Git hooks for code quality
![skill]( "skill you will learn")
- Implementing version control workflows
- Managing branches and resolving conflicts
- Collaborating using GitHub (with Copilot assistance)
- Applying Git best practices in team environments
- Implementing Git hooks and automation
Module 4: CI/CD Fundamentals & Pipeline Design
10 Topics
Topics
- Continuous Integration Principles & Benefits
- Continuous Delivery vs Continuous Deployment
- CI/CD Pipeline Architecture & Stages
- Build Automation & Artifact Management
- Automated Testing in CI/CD (Unit, Integration, E2E)
- Code Quality Gates & Static Analysis
- Deployment Strategies: Blue-Green, Canary, Rolling
- Pipeline as Code Concepts
- CI/CD Security Considerations
- Metrics & KPIs for CI/CD
![skill]( "Hands On Experience")
- Design multi-stage CI/CD pipeline architecture
- Create a simple CI pipeline with automated builds
- Implement automated testing in pipeline
- Set up artifact repository
- Simulate blue-green deployment strategy
- Build a deployment pipeline decision tree
- Design pipeline for microservices application
![skill]( "skill you will learn")
- Designing CI/CD pipelines for various scenarios
- Understanding pipeline stages and best practices
- Implementing automated testing strategies
- Applying CI/CD patterns and anti-patterns
- Planning deployment strategies
Module 5: CI/CD Implementation with Jenkins - I
13 Topics
Topics
- Introduction to Continuous Integration
- Introduction to Jenkins
- History of Jenkins
- Components of Jenkins
- Jenkins Architecture & Installation
- Jenkins Server Architecture
- Jenkins Master-Slave Architecture
- Distributed Builds & Jenkins Agents
- Freestyle vs Pipeline Jobs
- Managing Plugins in Jenkins
- Jenkins Plugins: Git, Maven, Docker, SonarQube
- Jenkins Security & Access Control
- Jenkins Credentials
![skill]( "Hands On Experience")
- Installing Jenkins in Ubuntu
- Creating Jenkins Job
- Managing Jenkins Plugins
- Managing Jenkins Users and Administration
- Configuring Notifications in Jenkins
![skill]( "skill you will learn")
- Installing and configuring Jenkins
- Understanding Jenkins architecture
- Managing plugins and tool integrations in Jenkins
- Configuring security and access control
- Creating and managing Jenkins jobs"
Module 6: CI/CD Implementation with Jenkins - II
12 Topics
Topics
- Overview of Deployment
- Jenkins Build Pipeline
- Parallel Jenkins Build
- Jenkinsfile & Pipeline as Code
- Jenkins Shared Libraries
- Jenkins Integration with Cloud Platforms
- Automating Deployment Using Jenkins
- Continuous Deployment Using Jenkins Pipelines
- Monitoring Jenkins Jobs and Pipelines
- Configuring Metrics and Logs in Jenkins
- Reporting and Code Analysis Plugin
- Code Coverage in Jenkins
![skill]( "Hands On Experience")
- Building a CI/CD Pipeline with Jenkinsfile
- Managing Build Artifacts and Pipelines
- Automated Testing and Deployment
- Jenkins Monitoring & Metrics
- Jenkins Code Quality & Reporting
![skill]( "skill you will learn")
- Designing CI/CD pipelines using Jenkins
- Automating testing and deployment workflows
- Managing build artifacts and optimizing pipeline execution
- Monitoring pipelines and system performance
- Implementing code quality analysis and reporting
Module 7: Containerization using Docker
10 Topics
Topics
- Introduction to Containerization & Benefits
- Docker Architecture: Images, Containers, Registry
- Docker Installation & Configuration
- Docker Images: Building, Tagging, Pushing
- Dockerfile Best Practices & Multi-Stage Builds
- Container Lifecycle Management
- Docker Networking: Bridge, Host, Overlay
- Docker Volumes & Data Persistence
- Container Resource Management
- Docker Logging & Debugging
![skill]( "Hands On Experience")
- Install Docker on Linux
- Run your first container
- Create custom Dockerfile for web application with Copilot
- Build multi-stage Docker images
- Push images to Docker Hub
- Configure container networking
- Implement volume mounting for data persistence
- Containerize a 3-tier application
- Debug container issues
![skill]( "skill you will learn")
- Understanding containerization concepts
- Building and managing Docker images
- Running and orchestrating containers
- Implementing Docker networking and storage
- Using Copilot for Dockerfile generation
Module 8: Docker Advanced, Compose & Registry
10 Topics
Topics
- Docker Image Optimization Techniques
- Docker Compose: Architecture & Syntax
- Multi-Container Application Management
- Docker Compose for Development & Testing
- Docker Security: Image Scanning, Secrets
- Vulnerability Scanning with Trivy
- Docker Registry: Harbor, ECR, ACR
- Docker in CI/CD Integration
- Docker Buildx & Multi-Platform Images
- Container Runtime Security
![skill]( "Hands On Experience")
- Optimize Docker image size (reduce by 70%+)
- Create Docker Compose file for microservices with Copilot
- Deploy multi-tier application with Docker Compose
- Configure custom networks and service discovery
- Implement Docker secrets management
- Scan images for vulnerabilities using Trivy
- Build automated Docker image pipeline
- Set up private Docker registry
![skill]( "skill you will learn")
- Optimizing Docker images for production
- Orchestrating multi-container applications
- Implementing Docker security best practices
- Managing private Docker registries
- Integrating Docker in CI/CD pipelines
Module 9: Container Orchestration using Kubernetes
10 Topics
Topics
- Kubernetes Architecture: Control Plane, Nodes
- Kubernetes Objects: Pods, Services, Deployments
- Kubernetes Installation: Minikube, kind, kubeadm
- kubectl Commands & Operations
- YAML Manifests for Kubernetes
- ReplicaSets & Deployments
- Services & Networking in Kubernetes
- ConfigMaps & Secrets Management
- Labels, Selectors & Annotations
- Kubernetes Namespaces
![skill]( "Hands On Experience")
- Set up Minikube cluster locally
- Deploy first pod using kubectl
- Create deployment for web application with Copilot
- Expose application using Services (ClusterIP, NodePort, LoadBalancer)
- Scale deployments horizontally
- Implement rolling updates and rollbacks
- Configure ConfigMaps and Secrets
- Deploy multi-tier application on Kubernetes
![skill]( "skill you will learn")
- Deploying and managing Kubernetes clusters
- Understanding Kubernetes architecture
- Deploying applications on Kubernetes
- Implementing basic Kubernetes operations
- Using Copilot for K8s YAML manifest generation
Module 10: Kubernetes Advanced Concepts
10 Topics
Topics
- StatefulSets & DaemonSets
- Persistent Volumes & Persistent Volume Claims
- Storage Classes & Dynamic Provisioning
- Ingress Controllers & Ingress Rules (NGINX, Traefik)
- Horizontal & Vertical Pod Autoscaling
- Resource Quotas & Limits
- Multi-Tenancy & Namespaces
- Helm: Package Manager for Kubernetes
- Helm Charts: Structure & Best Practices
- Kubernetes RBAC & Security
![skill]( "Hands On Experience")
- Deploy stateful application using StatefulSets
- Configure persistent storage for database
- Set up NGINX Ingress Controller
- Create Ingress rules for multiple services
- Implement Horizontal Pod Autoscaler (HPA)
- Set up resource quotas and limits
- Package application using Helm charts with Copilot
- Deploy microservices with Helm
- Configure RBAC for cluster security
![skill]( "skill you will learn")
- Implementing advanced Kubernetes patterns
- Configuring persistent storage solutions
- Setting up ingress controllers
- Packaging applications with Helm
- Applying resource management and autoscaling
Module 11: Terraform for Infrastructure Automation
10 Topics
Topics
- Introduction to Infrastructure as Code
- Terraform Architecture & Workflow
- HCL (HashiCorp Configuration Language)
- Terraform Providers (AWS, Azure, GCP)
- Resources, Variables & Outputs
- Terraform State Management
- Remote State & Backend Configuration
- Terraform Modules & Reusability
- Terraform Workspaces for Environments
- Terraform Cloud & Enterprise
![skill]( "Hands On Experience")
- Install and configure Terraform
- Provision EC2 instances on AWS with Copilot assistance
- Create VPC, subnets, and security groups
- Build reusable Terraform modules
- Implement remote state with S3 backend
- Provision Azure resources using Terraform
- Create multi-environment infrastructure with workspaces
- Implement Terraform pipeline in CI/CD
![skill]( "skill you will learn")
- Provisioning infrastructure using code
- Managing cloud resources declaratively
- Implementing Terraform best practices
- Version controlling infrastructure changes
- Using Copilot for Terraform code generation
Module 12: Ansible for Configuration Management
12 Topics
Topics
- Introduction to Configuration Management
- Ansible Architecture: Control Node, Managed Nodes
- Ansible Installation & Configuration
- Inventory Management (Static & Dynamic)
- Ad-hoc Commands
- Ansible Playbooks: YAML Syntax
- Ansible Modules & Collections
- Variables, Facts & Conditionals
- Ansible Roles & Galaxy
- Ansible Vault for Secrets
- Ansible Tower/AWX
![skill]( "Hands On Experience")
- Install Ansible and configure inventory
- Execute ad-hoc commands on multiple servers
- Write playbook to install web server with Copilot
- Deploy application using Ansible
- Create reusable Ansible roles
- Implement multi-tier application deployment
- Use Ansible Vault for credentials
- Integrate Ansible with Jenkins pipeline
- Combine Terraform + Ansible workflow
![skill]( "skill you will learn")
- Automating configuration management
- Writing Ansible playbooks
- Managing multiple servers efficiently
- Integrating Ansible with CI/CD pipelines
- Combining Terraform and Ansible
Module 13: AWS DevOps Services & Implementation
11 Topics
Topics
- AWS Cloud Fundamentals for DevOps
- AWS CodePipeline: Architecture & Setup
- AWS CodeBuild: Build Automation
- AWS CodeDeploy: Deployment Strategies
- AWS CodeCommit & CodeArtifact
- EC2, VPC, S3, RDS for DevOps
- AWS ECS & EKS: Container Orchestration
- AWS Lambda & Serverless DevOps
- AWS CloudFormation for IaC
- AWS CloudWatch: Monitoring & Logging
- AWS Systems Manager & Parameter Store
![skill]( "Hands On Experience")
- Build end-to-end pipeline with AWS CodePipeline
- Configure AWS CodeBuild for multi-language builds
- Deploy application using AWS CodeDeploy
- Provision AWS infrastructure with Terraform
- Set up EKS cluster and deploy containerized apps
- Implement blue-green deployment on AWS
- Build serverless CI/CD pipeline with Lambda
- Configure CloudWatch dashboards and alarms
![skill]( "skill you will learn")
- Deploying DevOps solutions on AWS
- Utilizing AWS native DevOps services
- Implementing CI/CD on AWS platform
- Managing AWS infrastructure with DevOps tools
Module 14: Azure DevOps & Cloud Integration
11 Topics
Topics
- Introduction to Azure DevOps Services
- Azure Pipelines Architecture (Classic vs YAML)
- Multi-Stage Pipelines & Templates
- Azure Repos, Boards & Test Plans
- Azure Kubernetes Service (AKS) Deep Dive
- Azure Container Instances (ACI)
- Azure Functions & Serverless
- Azure Resource Manager (ARM) Templates & Bicep
- Azure Monitor & Application Insights
- Azure Key Vault Integration
- GitHub Copilot for Azure DevOps
![skill]( "Hands On Experience")
- Create Azure DevOps organization and project
- Build YAML-based multi-stage pipeline
- Deploy application to Azure App Service
- Create and manage AKS cluster
- Deploy ARM/Bicep templates for infrastructure
- Configure Azure Monitor for comprehensive monitoring
- Integrate Application Insights for APM
- Set up Azure Key Vault for secrets management
![skill]( "skill you will learn")
- Building CI/CD pipelines in Azure DevOps
- Implementing YAML pipelines
- Deploying to Azure cloud services
- Integrating Azure services with DevOps workflows
Module 15: Monitoring, Logging & Observability
11 Topics
Topics
- Monitoring vs Observability: Three Pillars (Metrics, Logs, Traces)
- Prometheus: Architecture & Installation
- Metrics Collection & PromQL
- Grafana: Dashboards & Visualizations
- ELK Stack: Elasticsearch, Logstash, Kibana
- Loki for Log Aggregation
- Application Performance Monitoring (APM)
- Distributed Tracing with Jaeger/Zipkin
- Alerting & Incident Management
- SLIs, SLOs, SLAs & Error Budgets
- OpenTelemetry Overview
![skill]( "Hands On Experience")
- Install Prometheus and exporters
- Configure metrics collection for applications
- Create Grafana dashboards for system metrics
- Set up ELK/Loki for centralized logging
- Configure log shipping from applications
- Create log parsing and analysis queries
- Implement alerting rules in Prometheus
- Set up distributed tracing for microservices
- Define SLOs and create error budget dashboards
![skill]( "skill you will learn")
- Implementing comprehensive monitoring solutions
- Setting up centralized logging
- Creating dashboards and alerts
- Applying observability best practices
- Understanding the three pillars of observability
Module 16: DevSecOps & Security Automation
11 Topics
Topics
- Introduction to DevSecOps & Shift-Left Security
- Static Application Security Testing (SAST)
- Dynamic Application Security Testing (DAST)
- Software Composition Analysis (SCA)
- Container Security Scanning (Trivy, Clair)
- Infrastructure Security: CIS Benchmarks
- Secrets Management: HashiCorp Vault, Azure Key Vault
- Policy as Code: OPA, Kyverno
- Security in CI/CD Pipelines
- Compliance as Code
- AI-Powered Security Analysis
![skill]( "Hands On Experience")
- Integrate SonarQube for code quality & security
- Implement SAST scanning in pipeline
- Scan Docker images for vulnerabilities
- Configure Trivy/Snyk for container scanning
- Set up HashiCorp Vault for secrets
- Implement policy as code with OPA/Kyverno
- Scan infrastructure code with Checkov/tfsec
- Create security-hardened CI/CD pipeline
- Implement automated compliance checks
![skill]( "skill you will learn")
- Integrating security into DevOps pipelines
- Implementing security scanning tools
- Applying shift-left security practices
- Managing secrets and credentials securely
- Automating compliance checks
Module 17: GitOps & Progressive Delivery
11 Topics
Topics
- Introduction to GitOps Principles
- GitOps vs Traditional DevOps
- ArgoCD: Architecture & Installation
- Flux CD: Setup & Configuration
- Declarative Infrastructure with GitOps
- Application Sets & Multi-Cluster Management
- Progressive Delivery: Canary, Blue-Green
- Argo Rollouts for Advanced Deployments
- Feature Flags & A/B Testing
- Rollback Strategies & Self-Healing
- GitOps Best Practices & Security
![skill]( "Hands On Experience")
- Install and configure ArgoCD
- Deploy application using GitOps workflow
- Set up automated sync from Git to Kubernetes
- Implement canary deployment with Argo Rollouts
- Create blue-green deployment strategy
- Configure progressive delivery pipelines
- Implement feature flags with Flagsmith
- Manage multi-environment deployments with GitOps
- Build self-healing infrastructure
![skill]( "skill you will learn")
- Implementing GitOps workflows
- Using ArgoCD for continuous delivery
- Applying progressive delivery techniques
- Automating Kubernetes deployments
- Implementing self-healing infrastructure
Module 18: AIOps: Intelligent IT Operations
11 Topics
Topics
- Introduction to AIOps: Concepts & Benefits
- AIOps Platforms Overview (Dynatrace, Datadog, Splunk)
- Machine Learning for IT Operations
- Anomaly Detection in Monitoring
- Predictive Analytics for System Failures
- AI-Powered Log Analysis & Pattern Recognition
- Intelligent Alerting & Noise Reduction
- Root Cause Analysis with AI
- Automated Incident Triage & Response
- Capacity Planning with ML
- AIOps Integration with Existing Tools
![skill]( "Hands On Experience")
- Implement anomaly detection with Prometheus & ML
- Build AI-powered log analysis system
- Create intelligent alerting with reduced false positives
- Implement predictive failure analysis
- Set up automated root cause analysis
- Build ML model for capacity prediction
- Automate incident triage with AI
- Create intelligent dashboard with ML insights
- Integrate AIOps with existing monitoring stack
![skill]( "skill you will learn")
- Applying AI/ML in IT operations
- Implementing predictive analytics for failures
- Automating incident detection and response
- Reducing alert noise with AI
- Building intelligent monitoring systems
Module 19: AI-Powered Software Engineering
11 Topics
Topics
- AI in Software Engineering: Overview & Trends
- AI Coding Assistants: Copilot, CodeWhisperer, Tabnine
- AI-Powered Code Review & Analysis
- Automated Test Generation with AI
- AI for Code Refactoring & Optimization
- Natural Language to Code Conversion
- AI-Powered Documentation Generation
- Code Security Analysis with AI
- AI for Bug Detection & Resolution
- ChatOps & Conversational AI for DevOps
- Building AI-Enhanced CI/CD Pipelines
![skill]( "Hands On Experience")
- Compare different AI coding assistants
- Implement AI-powered code review in pipeline
- Use AI for test case generation
- Generate unit tests automatically with AI
- Build AI-powered documentation workflow
- Create ChatOps bot with Slack/Teams integration
- Implement AI-based code quality gates
- Build natural language to infrastructure workflow
- Create AI-enhanced debugging system
![skill]( "skill you will learn")
- Leveraging AI for software development lifecycle
- Implementing AI-assisted code review
- Using AI for automated testing
- Applying AI for code quality and documentation
- Building AI-enhanced development workflows
Module 20: GenAI for DevOps and Platform Engineering
9 Topics
Topics
- Generative AI in DevOps: Advanced Use Cases
- LLMs for Infrastructure Code Generation
- AI Agents for DevOps Automation
- Custom GPT/Claude for DevOps Tasks
- Platform Engineering & Internal Developer Platforms (IDP)
- Self-Service Infrastructure Portals (Backstage)
- FinOps: AI-Driven Cost Optimization
- Emerging Trends: WebAssembly, eBPF, Service Mesh
- Building AI-Native DevOps Culture
![skill]( "Hands On Experience")
- Build custom AI assistant for DevOps tasks
- Generate infrastructure code using LLMs
- Create AI agent for pipeline optimization
- Design Internal Developer Platform architecture
- Implement self-service portal with Backstage
- Build cost optimization dashboard with AI
![skill]( "skill you will learn")
- Leveraging Generative AI for DevOps automation
- Designing internal developer platforms
- Implementing FinOps practices
- Building end-to-end DevOps pipeline
Module 21: Advanced Kubernetes Operations & Service Mesh (Self-Paced)
12 Topics
Topics
- Kubernetes Operators: Concepts & Use Cases
- Custom Resource Definitions (CRDs)
- Building Custom Operators with Operator SDK
- Service Mesh Introduction: Why Service Mesh?
- Istio Architecture: Control Plane & Data Plane
- Istio Installation & Configuration
- Traffic Management: Virtual Services, Destination Rules
- mTLS & Security Policies
- Observability with Istio: Kiali, Jaeger Integration
- Linkerd Overview & Comparison
- Multi-Cluster Service Mesh
- Service Mesh Best Practices
![skill]( "Hands On Experience")
- Create Custom Resource Definition (CRD)
- Build simple Kubernetes Operator
- Install Istio on Kubernetes cluster
- Configure traffic splitting (Canary with Istio)
- Implement circuit breaker pattern
- Enable mTLS between services
- Set up Kiali dashboard for mesh visualization
- Implement rate limiting with Istio
- Configure fault injection for testing
- Deploy application across multiple clusters
![skill]( "skill you will learn")
- Implementing Kubernetes Operators and Custom Resources
- Configuring service mesh with Istio/Linkerd
- Implementing advanced traffic management
- Applying mTLS and zero-trust security
- Managing multi-cluster Kubernetes deployments
Module 22: Chaos Engineering & Site Reliability Engineering (SRE) (Self-Paced)
14 Topics
Topics
- Introduction to Chaos Engineering
- Chaos Engineering Principles & Practices
- Chaos Monkey & Netflix Simian Army
- Chaos Toolkit & LitmusChaos
- Gremlin for Enterprise Chaos
- Designing Chaos Experiments
- Site Reliability Engineering (SRE) Fundamentals
- SRE vs DevOps: Complementary Practices
- Service Level Indicators (SLIs)
- Service Level Objectives (SLOs)
- Error Budgets & Budget Policies
- Incident Management & Postmortems
- Toil Reduction Strategies
- On-Call Best Practices
![skill]( "Hands On Experience")
- Install LitmusChaos on Kubernetes
- Design and run pod failure experiment
- Implement network chaos scenarios
- Create CPU/Memory stress tests
- Define SLIs for sample application
- Calculate and implement SLOs
- Set up error budget tracking dashboard
- Conduct chaos experiment on microservices
- Write postmortem document for incident
- Implement automated rollback on chaos detection
![skill]( "skill you will learn")
- Applying chaos engineering principles
- Implementing reliability testing in production
- Designing and implementing SRE practices
- Creating error budgets and SLOs
- Building resilient systems through controlled failure
Module 23: Advanced Terraform & Multi-Cloud IaC (Self-Paced)
14 Topics
Topics
- Advanced Terraform Patterns & Anti-Patterns
- Terraform Functions & Expressions
- Dynamic Blocks & For Each
- Terragrunt: DRY Infrastructure Code
- Terragrunt Configuration & Best Practices
- Multi-Cloud Infrastructure Management
- Terraform Cloud & Enterprise Features
- Sentinel Policy as Code
- Writing Sentinel Policies
- Infrastructure Testing with Terratest
- Terraform Import & State Surgery
- Terraform CDK (CDKTF) Introduction
- Pulumi Overview & Comparison
- Cost Estimation with Infracost
![skill]( "Hands On Experience")
- Implement complex Terraform with dynamic blocks
- Set up Terragrunt for multi-environment
- Create reusable Terragrunt modules
- Deploy infrastructure to AWS and Azure simultaneously
- Write Sentinel policies for compliance
- Implement Terratest for module testing
- Import existing infrastructure to Terraform
- Perform state migration between backends
- Use Infracost for cost estimation
- Create CDKTF infrastructure in Python
![skill]( "skill you will learn")
- Implementing advanced Terraform patterns
- Using Terragrunt for DRY infrastructure
- Managing multi-cloud infrastructure
- Implementing policy as code with Sentinel
- Automating infrastructure testing
Module 24: Cloud Cost Optimization & FinOps (Self-Paced)
15 Topics
Topics
- Introduction to FinOps: Framework & Principles
- FinOps Lifecycle: Inform, Optimize, Operate
- Cloud Cost Fundamentals: Pricing Models
- Reserved Instances & Savings Plans
- Spot/Preemptible Instances Strategies
- AWS Cost Explorer & Cost Anomaly Detection
- Azure Cost Management & Budgets
- Kubecost for Kubernetes Cost Management
- Right-sizing Resources
- Idle Resource Detection & Cleanup
- Tagging Strategies for Cost Allocation
- Showback & Chargeback Models
- AI-Powered Cost Optimization
- Cost Governance & Policies
- Building FinOps Culture
![skill]( "Hands On Experience")
- Analyze cloud costs using AWS Cost Explorer
- Set up Azure budgets and alerts
- Implement tagging strategy for cost allocation
- Install and configure Kubecost
- Identify and terminate idle resources
- Calculate potential savings from Reserved Instances
- Implement Spot instances for non-critical workloads
- Create cost optimization recommendations report
- Build cost dashboard with Grafana
- Implement automated cost alerts and actions
![skill]( "skill you will learn")
- Implementing FinOps practices and principles
- Optimizing cloud costs across AWS and Azure
- Using AI for cost prediction and optimization
- Implementing cost governance and allocation
- Designing cost-aware architectures
DevOps Training Course with Gen AI Description
What will I learn in the Advanced DevOps Certification Training with GenAI?
This industry-leading program covers the complete DevOps lifecycle integrated with Generative AI capabilities. You'll master essential DevOps tools including Docker, Kubernetes, Jenkins, Terraform, Ansible, and cloud platforms like AWS, Azure, and GCP. What sets this course apart is the GenAI integration. You'll learn to leverage AI-powered tools for intelligent automation, code generation, predictive monitoring, and self-healing infrastructure. By completion, you'll have hands-on experience with 30+ industry tools, GenAI-assisted DevOps workflows, and 10 real-world projects.
What is DevOps?
DevOps is a cultural philosophy and a set of practices supported by tools that bring together software development (Dev) and IT operations (Ops). Its goal is to automate and accelerate the software delivery lifecycle by improving collaboration, communication, and continuous integration, resulting in faster, more reliable releases.
By breaking down traditional silos between teams, DevOps enables developers and operations teams to work together across the entire lifecycle; from planning and development to deployment and monitoring to ensure that high-quality software can be delivered quickly and consistently to meet customer needs.
How is Generative AI integrated into this DevOps training program?
GenAI is woven throughout the curriculum, not treated as an afterthought. You'll learn to use AI coding assistants like GitHub Copilot for writing Infrastructure as Code and automation scripts. The course covers AI-powered monitoring and AIOps platforms for intelligent alerting and root cause analysis. You'll explore ChatOps integration, natural language infrastructure queries, and GenAI tools for documentation generation, incident summarization, and automated runbook creation. This prepares you for the future of DevOps where AI augments every stage of the software delivery lifecycle.
Is this Advanced DevOps course suitable for beginners with no prior experience?
This course is designed with a progressive learning path that welcomes motivated beginners while also challenging experienced professionals. We start with DevOps fundamentals and Linux basics before advancing to complex topics and GenAI integration. If you have basic programming knowledge and understand how software applications work, you're ready to begin. Our hands-on labs break down complex concepts into practical exercises, and no prior DevOps or AI experience is require
Is it worth learning DevOps with GenAI?
Yes, learning DevOps with GenAI is absolutely worth it. As a rapidly growing field with significant potential to revolutionize how organizations build, deploy, and manage software, it is regarded as one of the most valuable skill combinations for the future of work. GenAI is transforming DevOps by automating complex tasks, enabling intelligent decision-making, and facilitating seamless human-AI collaboration across CI/CD pipelines, infrastructure management, and incident response. Professionals with both DevOps and GenAI skills are commanding premium salaries across industries.
What are the prerequisites for this Advanced DevOps Certification Training with GenAI?
In order to complete this course successfully, participants need to have a basic understanding of programming concepts (preferably Python or Shell scripting), Linux fundamentals, and general IT operations. Familiarity with cloud computing basics is helpful but not mandatory. However, learners will be provided with self-learning refresher material on Linux, Git, and foundational DevOps concepts before beginning the live classes. No prior experience with Generative AI is required as GenAI fundamentals are covered within the course.
Why should professionals become DevOps Engineers with GenAI skills?
Upon completing the Advanced DevOps Certification Training with GenAI, participants will learn to design and build automated CI/CD pipelines using tools like Jenkins, GitHub Actions, and ArgoCD. They will implement containerization with Docker, orchestration with Kubernetes, and Infrastructure as Code using Terraform and Ansible.
The course covers GenAI integration with tools like GitHub Copilot and Amazon CodeWhisperer for intelligent code generation, AIOps platforms for predictive monitoring, and ChatOps for natural language automation. Participants will also learn cloud deployment on AWS, Azure, and GCP, along with observability using Prometheus, Grafana, and ELK Stack. Hands-on projects ensure practical expertise in building scalable, real-world DevOps solutions powered by AI.
Who should take this Advanced DevOps Certification Training with GenAI?
The Advanced DevOps Certification Training with GenAI is ideal for IT enthusiasts, developers, and professionals looking to build automated, intelligent software delivery pipelines. It is best suited for:
- Software Developers wanting to expand into DevOps
- System Administrators transitioning to cloud and automation roles
- IT Operations professionals seeking modern DevOps practices
- DevOps Engineers looking to upgrade their skills with GenAI
- Cloud Engineers wanting to integrate AI into infrastructure management
- Freshers who want to leverage DevOps and GenAI for career growth
Is GenAI-powered DevOps the future?
Yes, GenAI-powered DevOps is shaping the future of software delivery by enabling intelligent, automated systems that can reason, predict, and act independently. Unlike traditional DevOps, which relies heavily on manual scripting and rule-based automation, GenAI-powered DevOps dynamically adapts, learns from patterns, and interacts with infrastructure intelligently.
As industries move toward faster releases, zero-downtime deployments, and self-healing infrastructure, GenAI will play a crucial role in revolutionizing CI/CD pipelines, incident management, security automation, and infrastructure optimization.
How will I execute the practicals in this Advanced DevOps Certification Training with GenAI?
A step-by-step guide for setting up environments, configuring DevOps tools, and integrating GenAI assistants will be provided in the Learning Management System (LMS). Participants will have access to cloud-based lab environments for practicing Docker, Kubernetes, Terraform, CI/CD pipelines, and GenAI tools without complex local setups.
Edureka's Support Team will be available 24/7 to assist learners in case they have any questions or face any technical issues during the practicals.
What are the system requirements for this Advanced DevOps Certification Training with GenAI?
The system requirements for this Advanced DevOps Certification Training with GenAI include:
Hardware Requirements:
- CPU: Multi-core processor (minimum 2 cores, 4 or more recommended)
- Memory (RAM): At least 8 GB (16 GB recommended for running Docker and Kubernetes locally)
- Storage: Minimum 50 GB of free disk space for container images and tools
- GPU: Not required for this course
Software Requirements:
- Operating System: Windows 10/11, macOS, or Linux (Ubuntu 20.04+ recommended for DevOps development)
- Programming Language: Basic proficiency in Python or Shell scripting
- Development Tools: VS Code, Terminal/Command Line
- Virtualization: Docker Desktop (installation guided in course)
- Browser: Chrome or Firefox for accessing cloud consoles and GenAI tools
- Package Management: Familiarity with pip, npm, or apt is helpful
DevOps Training Projects
Build an AI-Powered CI/CD Pipeline Generator
Develop an intelligent CI/CD pipeline generator using GitHub Copilot and LLMs that automatically creates optimized Jenkins and GitHub Actions pipelines based on project requireme....Create an AIOps-Powered Incident Response System
Build an intelligent incident management system that uses machine learning to detect anomalies in production environments, predict system failures, and automatically trigger reme....Design a GitOps-Based Multi-Cloud Deployment Platform
Develop a comprehensive GitOps platform using ArgoCD and Flux CD that enables declarative, automated deployments across AWS, Azure, and GCP. Implement progressive delivery strate....Build a Kubernetes Resource Optimization Agent with AI
Create an AI-powered agent using Python and Kubernetes APIs that continuously monitors cluster resource utilization, identifies over-provisioned and under-utilized resources, and....Develop an Intelligent Security Scanning Pipeline
Build a comprehensive DevSecOps pipeline that integrates multiple security scanning tools (SonarQube, Trivy, Snyk, Checkov) with AI-powered vulnerability prioritization. The syst....Create a Self-Service Infrastructure Portal with Backstage
Design and implement an Internal Developer Platform (IDP) using Backstage that provides self-service infrastructure provisioning, template-based application scaffolding, and AI-a....Build an AI-Powered Log Analysis and Troubleshooting System
Develop an intelligent log analysis platform using ELK Stack and machine learning that automatically identifies patterns in application and infrastructure logs, correlates events....Design a Container Image Security and Compliance Scanner
Create an automated container security solution that scans Docker images for vulnerabilities, malware, and compliance violations before deployment. Use AI to assess risk scores, ....Develop a Multi-Region Disaster Recovery Automation System
Build an automated disaster recovery (DR) system for Kubernetes applications that uses Terraform and ArgoCD to maintain synchronized infrastructure across multiple regions. Imple....Create an Infrastructure-as-Code Testing and Validation Framework
Develop a comprehensive testing framework for Terraform and Ansible code using Terratest, Molecule, and AI-powered test generation. The system automatically generates test scenar....DevOps Certification
Edureka provides the Advanced DevOps GenAI certification upon successful course completion. The certification is valid for lifetime with no renewal required.
Complete all 20 live modules, pass quizzes, finish hands-on DevOps assignments and complete the capstone project with CI/CD pipeline, GitOps deployment, and AI integration.
This certification validates expertise in CI/CD automation, Kubernetes orchestration, infrastructure as code (Terraform/Ansible), cloud platforms (AWS/Azure), GitOps, AIOps, and AI-powered DevOps workflows. It opens opportunities in DevOps Engineering, SRE, Platform Engineering, and Cloud Architecture roles across finance, healthcare, retail, and technology industries.
You can apply for multiple job roles which includes: DevOps Engineer, Site Reliability Engineer (SRE), Platform Engineer, Cloud DevOps Engineer, Kubernetes Administrator, Infrastructure Automation Engineer, DevSecOps Engineer, AIOps Engineer, CI/CD Engineer, Cloud Solutions Architect, and more.
The certification requires understanding CI/CD, containerization, Kubernetes, infrastructure as code, and cloud platforms. With structured learning across 20 modules, 80+ hours of hands-on practice with Docker, Jenkins, Terraform, and GitHub Copilot, plus expert guidance, it's achievable for dedicated learners.
Yes, you receive access to all course materials including 20 live module recordings, 5 self-paced modules, hands-on labs, project templates, and regular updates on DevOps tools and AI automation trends.
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Read learner testimonials
I had an excellent experience with Edureka during the DevOps Certification Training Course. The study materials were up-to-date, and the instructors were incredibly helpful, providing all the necessary information and assistance. I believe Edureka is one of the best platforms to choose for these courses. Highly recommended.
April 27, 2024
I have completed the DevOps Certification Training from Edureka, and it has been a transformative experience in understanding the principles and practices that drive efficient software development and deployment. This course is a game-changer for anyone looking to bridge the gap between development and operations teams. The training covered a wide array of topics, starting from the fundamental concepts of DevOps to advanced automation techniques. The instructors were knowledgeable and provided clear explanations, making complex concepts accessible even for those new to the field. The hands-on labs and real-world case studies were particularly valuable, allowing me to apply theoretical knowledge in practical scenarios.
January 03, 2024
Comprehensive and Practical DevOps Engineer Course - A Great Learning Experience! Specially thanks to instructor, really good support as well guidance and doing extra efforts if required. Another special thanks to Support Team, who has provided quick response, support & guidance on each ticket.
August 30, 2024
The DevOps Master program provided a comprehensive and hands-on learning experience, equipping me with the skills needed to streamline software development and deployment processes effectively. Highly recommended for professionals aiming to excel in DevOps practices.
May 09, 2024
I attended the demo session without any intention of joining a course. But the demo class was so impressive that I changed my mind to take a class with edureka. In the demo class the edureka! team making their word true with assisting doubts as well as teaching good. It's the best place to learn Andorid with 24*7 post class support from technical experts and at a affordable cost. Thank You Team edureka!
December 09, 2017
My experience with edureka about Python is much impressed, with such quality of the training. We get access to each day class recorded sessions after the live class. Edureka is very quick reactive towards the queries of each single user, they answer and follow-up to ensure you get the correct response to resolve your query. Online recording of the course is very useful as you can go back and refer at any time. You have access to course and materials for ever, which is really helpful. More over 24/7 quality support, which is very much important to any of us. I must thank to Edureka for giving such support. Thanks guys, cheerââ¬Â¦
December 09, 2017
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Edureka DevOps Training Advantage
| WHAT I NEED | EDUREKA ADVANTAGE | WHAT OTHERS OFFER |
|---|---|---|
| AI-integrated DevOps curriculum | ||
| Tool Coverage | ||
| Project-Based Learning | ||
| Industry Use Cases | ||
| DevSecOps Coverage | ||
| Platform Engineering Topics | ||
| Instructor-led Live Classes | ||
| Cloud Lab Exposure |
Edureka Advantage
What others offer
DevOps Training FAQs
What are the key features of Advanced DevOps Certification Training with GenAI training?
AI-First approach with GitHub Copilot from Module 2, coverage of 35+ tools (Jenkins, Kubernetes, Docker, Terraform, ArgoCD, AWS, Azure), dedicated AIOps and AI Software Engineering modules, platform engineering with Backstage, GitOps workflows, 10+ industry projects, and comprehensive capstone with microservices deployment.
Why should I enroll in Advanced DevOps Certification Training with GenAI training?
Master AI-powered DevOps automation with GitHub Copilot, Kubernetes, Terraform, ArgoCD, and AIOps platforms. Get hands-on experience with 20 comprehensive modules, 10+ industry projects, expert-led instruction. Opens doors to high-demand DevOps Engineer, SRE, and Platform Engineer roles with competitive salaries across technology, finance, healthcare, and retail industries.
What are the objectives of Advanced DevOps Certification Training with GenAI certification course?
Learn to design and deploy production-ready DevOps pipelines with AI automation using Jenkins, GitHub Actions, Docker, Kubernetes, Terraform, Ansible, and ArgoCD. Gain expertise in CI/CD, container orchestration, infrastructure as code, AWS/Azure cloud platforms, observability with Prometheus/Grafana, DevSecOps, GitOps, AIOps, and platform engineering with real-world projects and cost optimization (FinOps).
What is the role of a DevOps Engineer with AI expertise?
Design automated CI/CD pipelines, manage Kubernetes clusters, provision infrastructure with Terraform, use GitHub Copilot for code generation, implement AIOps for predictive monitoring, build self-service developer platforms, ensure DevSecOps practices, deploy with GitOps methodologies, and optimize cloud costs using AI-driven recommendations on AWS and Azure.
What if I miss a live Advanced DevOps Certification Training with GenAI class?
Access recorded sessions of all 20 live modules anytime. All hands-on Docker, Kubernetes, Jenkins, Terraform labs and demonstrations are available for review at your convenience.
What support is available after completing DevOps GenAI training?
Contact Edureka's support team for queries and access active community forums for DevOps best practices, networking, and ongoing help. Receive regular updates on new DevOps tools, Kubernetes features, and AI automation trends.
What skills will I acquire in Advanced DevOps Certification Training with GenAI training?
Master CI/CD with Jenkins and GitHub Actions, Docker containerization, Kubernetes orchestration, Terraform and Ansible for infrastructure as code, AWS and Azure cloud platforms, GitOps with ArgoCD, monitoring with Prometheus and Grafana, DevSecOps, GitHub Copilot for AI-assisted coding, AIOps for intelligent operations, and platform engineering.
How does DevOps GenAI certification help career advancement?
DevOps GenAI certification provides specialized skills in CI/CD automation, Kubernetes, cloud platforms, and AI-powered DevOps. Opens doors to DevOps Engineer, SRE, and Platform Engineer roles with 20-40% salary increases. Demonstrates expertise in Jenkins, Terraform, ArgoCD, AIOps, and GitHub Copilot for faster career progression to senior and leadership positions.
What tools and technologies are covered in DevOps GenAI certification?
Python, Bash scripting, YAML, Jenkins, GitHub Actions, Docker, Kubernetes, Helm, Terraform, Ansible, ArgoCD, Flux CD, Prometheus, Grafana, ELK Stack, AWS (EKS, CodePipeline), Azure (AKS, DevOps), GitHub Copilot, SonarQube, Trivy, HashiCorp Vault, and AIOps platforms.
Can beginners with minimal DevOps experience take this course?
Yes, if you have basic software development, Linux, and programming fundamentals (Python/scripting). The course starts with DevOps fundamentals and progresses systematically through Docker, Kubernetes, CI/CD, and cloud platforms, making it accessible for those transitioning from development, system administration, or IT operations roles.
Is DevOps Engineering a good career choice in 2026?
Yes, DevOps Engineering is highly promising with exponential demand across all industries. DevOps Engineers with AI automation skills command salaries of $100K-$180K+ in the US. The role offers clear progression to Senior DevOps Engineer, SRE, Platform Engineer, DevOps Architect, and leadership positions with excellent job security.
What is the future of AI in DevOps and automation?
AI is revolutionizing DevOps through AIOps (predictive monitoring, automated incident response), GitHub Copilot for infrastructure code generation, intelligent testing, self-healing systems, and automated cost optimization. AI-powered DevOps enables faster deployment, improved reliability, reduced manual intervention, and significant efficiency gains across CI/CD pipelines, Kubernetes management, and cloud operations.
What industries hire DevOps professionals with AI skills?
Technology and SaaS companies, financial services and banking (BFSI), healthcare and life sciences, e-commerce and retail, telecommunications, media and entertainment, manufacturing, government and public sector, and startups building cloud-native applications with Kubernetes and microservices architectures.
When do I get access to Advanced DevOps Certification Training with GenA course content?
Immediate access after signup to course materials, pre-requisite resources, community forums, and environment setup guides. Live sessions start as per your selected batch schedule.
What is the difference between DevOps and Platform Engineering?
DevOps focuses on CI/CD pipelines, automation, and collaboration between development and operations. Platform Engineering builds Internal Developer Platforms (IDP) with self-service infrastructure, standardized workflows, and developer experience optimization. Module 20 covers platform engineering with Backstage, enabling you to pursue both DevOps and Platform Engineer career paths.
Have more questions?
Course counsellors are available 24x7
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