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, and lifetime access. Opens doors to high-demand DevOps Engineer, SRE, and Platform Engineer roles with competitive salaries across technology, finance, healthcare, and retail industries.

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.

Is lifetime access included for course materials?

Yes, lifetime access to all 20 live module recordings, 5 self-paced modules (Service Mesh, Chaos Engineering, FinOps, Advanced Terraform, Career Development), lab environments, project templates, code repositories, infrastructure templates, and documentation with regular updates.

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.

Advanced DevOps Certification Training Course Online with GenAI

Name: Advanced DevOps Certification Training with GenAI
Item: Advanced DevOps Certification Training with GenAI
Rating: 5
Author: Vijay Kalkundri

Why should you take Advanced DevOps Certification Training with GenAI?

The global DevOps market, valued at USD 51.8 B in 2026, is projected to reach USD 93.13 B by 2035 at a CAGR of 5.6% - Business Research Insights

By 2027, 80% of organizations will adopt DevOps platforms, marking a major and rapid 25% jump compared to 2023 - Gartner

The average annual salary for a DevOps Engineer in the United States is USD 129,417, with top earners up to USD 200,000 - Indeed

Module 1: DevOps Fundamentals, Culture, and AI Transformation

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

Hands-on:

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

Skills 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

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Module 2: GitHub Copilot & AI-Assisted Development

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

Hands-on:

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

Skills 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

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Module 3: Version Control with Git & GitHub

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

Hands-on:

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

Skills 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

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Module 4: CI/CD Fundamentals & Pipeline Design

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

Hands-on:

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

Skills 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

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Module 5: Jenkins CI/CD Implementation

Topics:

Jenkins Architecture & Installation
Jenkins UI & Job Configuration
Freestyle vs Pipeline Jobs
Declarative Pipeline Syntax
Jenkinsfile & Pipeline as Code
Jenkins Plugins: Git, Maven, Docker, SonarQube
Distributed Builds & Jenkins Agents
Jenkins Security & Access Control
Jenkins Shared Libraries
Jenkins Integration with Cloud Platforms

Hands-on:

Install Jenkins on Linux server
Create freestyle job with Git integration
Build Java/Python application pipeline
Write Jenkinsfile for multi-stage pipeline using Copilot
Configure Jenkins agents for distributed builds
Implement parameterized builds
Set up Jenkins credentials and role-based access
Create shared library for reusable pipeline code

Skills you will learn:

Installing and configuring Jenkins
Creating declarative and scripted pipelines
Integrating Jenkins with Git, Docker, and build tools
Implementing advanced Jenkins features
Using Copilot for Jenkinsfile generation

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Module 6: GitHub Actions & Modern CI/CD

Topics:

• GitHub Actions Architecture & Concepts

• Workflow Syntax & Triggers

• Jobs, Steps & Actions

• Secrets & Environment Variables

• Matrix Builds & Strategy

• Caching & Artifacts

• Reusable Workflows & Composite Actions

• Self-Hosted Runners

• GitHub Actions Marketplace

• Security Best Practices for Actions

Hands-on:

• Create first GitHub Actions workflow

• Build multi-platform application with matrix builds

• Implement caching for faster builds

• Create reusable workflow for deployment

• Set up self-hosted runner

• Integrate Docker build and push

• Implement security scanning with CodeQL

• Compare workflow with equivalent Jenkins pipeline

Skills you will learn:

• Building workflows using GitHub Actions

• Implementing matrix builds and caching

• Creating reusable workflows and actions

• Integrating security scanning in workflows

• Comparing GitHub Actions with Jenkins

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Module 7: Docker Fundamentals & Best Practices

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

Hands-on:

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

Skills 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

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Module 8: Docker Advanced, Compose & Registry

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

Hands-on:

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

Skills 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

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Module 9: Kubernetes Fundamentals

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

Hands-on:

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

Skills 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

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Module 10: Kubernetes Advanced & Helm

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

Hands-on:

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

Skills you will learn:

Implementing advanced Kubernetes patterns
Configuring persistent storage solutions
Setting up ingress controllers
Packaging applications with Helm
Applying resource management and autoscaling

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Module 11: Terraform for Infrastructure Automation

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

Hands-on:

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

Skills 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

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Module 12: Ansible for Configuration Management

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

Hands-on:

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

Skills you will learn:

Automating configuration management
Writing Ansible playbooks
Managing multiple servers efficiently
Integrating Ansible with CI/CD pipelines
Combining Terraform and Ansible

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Module 13: AWS DevOps Services & Implementation

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

Hands-on:

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

Skills 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

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Module 14: Azure DevOps & Cloud Integration

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

Hands-on:

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

Skills you will learn:

Building CI/CD pipelines in Azure DevOps
Implementing YAML pipelines
Deploying to Azure cloud services
Integrating Azure services with DevOps workflows

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Module 15: Monitoring, Logging & Observability

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

Hands-on:

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

Skills 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

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Module 16: DevSecOps & Security Automation

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

Hands-on:

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

Skills 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

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Module 17: GitOps & Progressive Delivery

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

Hands-on:

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

Skills you will learn:

Implementing GitOps workflows
Using ArgoCD for continuous delivery
Applying progressive delivery techniques
Automating Kubernetes deployments
Implementing self-healing infrastructure

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Module 18: AIOps: Intelligent IT Operations

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

Hands-on:

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

Skills 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

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Module 19: AI-Powered Software Engineering

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

Hands-on:

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

Skills 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

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Module 20: GenAI for DevOps and Platform Engineering

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

Hands-on:

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

Skills you will learn:

Leveraging Generative AI for DevOps automation
Designing internal developer platforms
Implementing FinOps practices
Building end-to-end DevOps pipeline

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Module 21: Advanced Kubernetes Operations & Service Mesh (Self-Paced)

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

Hands-on:

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

Skills 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

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Module 22: Chaos Engineering & Site Reliability Engineering (SRE) (Self-Paced)

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

Hands-on:

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

Skills 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

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Module 23: Advanced Terraform & Multi-Cloud IaC (Self-Paced)

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

Hands-on:

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

Skills 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

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Module 24: Cloud Cost Optimization & FinOps (Self-Paced)

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

Hands-on:

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

Skills 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

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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

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Advanced DevOps Certification Training with GenAI

Why should you take Advanced DevOps Certification Training with GenAI?

Instructor-led Advanced DevOps Certification Training with GenAI live online classes

Financing Options

Curriculum

Module 1: DevOps Fundamentals, Culture, and AI Transformation

Module 2: GitHub Copilot & AI-Assisted Development

Module 3: Version Control with Git & GitHub

Module 4: CI/CD Fundamentals & Pipeline Design

Module 5: Jenkins CI/CD Implementation

Module 6: GitHub Actions & Modern CI/CD

Module 7: Docker Fundamentals & Best Practices

Module 8: Docker Advanced, Compose & Registry

Module 9: Kubernetes Fundamentals

Module 10: Kubernetes Advanced & Helm

Module 11: Terraform for Infrastructure Automation

Module 12: Ansible for Configuration Management

Module 13: AWS DevOps Services & Implementation

Module 14: Azure DevOps & Cloud Integration

Module 15: Monitoring, Logging & Observability

Module 16: DevSecOps & Security Automation

Module 17: GitOps & Progressive Delivery

Module 18: AIOps: Intelligent IT Operations

Module 19: AI-Powered Software Engineering

Module 20: GenAI for DevOps and Platform Engineering

Module 21: Advanced Kubernetes Operations & Service Mesh (Self-Paced)

Module 22: Chaos Engineering & Site Reliability Engineering (SRE) (Self-Paced)

Module 23: Advanced Terraform & Multi-Cloud IaC (Self-Paced)

Module 24: Cloud Cost Optimization & FinOps (Self-Paced)

DevOps Training Course with Gen AI Description

What will I learn in the Advanced DevOps Certification Training with GenAI?

What is DevOps?

How is Generative AI integrated into this DevOps training program?

Is this Advanced DevOps course suitable for beginners with no prior experience?

Is it worth learning DevOps with GenAI?

What are the prerequisites for this Advanced DevOps Certification Training with GenAI?

Why should professionals become DevOps Engineers with GenAI skills?

Who should take this Advanced DevOps Certification Training with GenAI?

Is GenAI-powered DevOps the future?

How will I execute the practicals in this Advanced DevOps Certification Training with GenAI?

What are the system requirements for this Advanced DevOps Certification Training with GenAI?

Testimonial Reviews

Video Reviews

DevOps Training FAQs

What are the key features of Advanced DevOps Certification Training with GenAI training?

Why should I enroll in Advanced DevOps Certification Training with GenAI training?

What are the objectives of Advanced DevOps Certification Training with GenAI certification course?

What is the role of a DevOps Engineer with AI expertise?

What if I miss a live Advanced DevOps Certification Training with GenAI class?

What support is available after completing DevOps GenAI training?

What skills will I acquire in Advanced DevOps Certification Training with GenAI training?

How does DevOps GenAI certification help career advancement?

What tools and technologies are covered in DevOps GenAI certification?

Can beginners with minimal DevOps experience take this course?

Is DevOps Engineering a good career choice in 2026?

What is the future of AI in DevOps and automation?

What industries hire DevOps professionals with AI skills?

When do I get access to Advanced DevOps Certification Training with GenA course content?

Is lifetime access included for course materials?

What is the difference between DevOps and Platform Engineering?

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