Linux, Cloud & DevOps Training Program with AI.
Build job-ready infrastructure, automation and deployment skills. Learn to work on Linux servers, configure AWS environments, automate repeatable tasks, containerise applications and deliver software through a practical DevOps workflow.
Hands-on labs, notes, learning references, project guidance and revision resources are included to support regular practice.
Help software run reliably, securely and repeatedly.
Cloud and DevOps work connects development with operations. You learn to configure servers, manage cloud resources, automate repetitive work, package applications, create delivery pipelines and troubleshoot issues when systems need attention.
Work confidently in Linux.
Use the terminal, manage files, users, permissions, services, processes, logs and scheduled tasks on Linux servers.
Create secure AWS environments.
Work with IAM, EC2, S3, VPC, networking, security groups, CloudWatch and cost-aware cloud practices.
Reduce manual effort.
Use Bash, Git, Jenkins and Ansible to make builds, server setup and deployments more repeatable.
Deliver containerised apps.
Use Docker and Kubernetes to package, run, scale, configure and troubleshoot applications in modern environments.
Think in repeatable systems, not one-time fixes.
DevOps is not only a list of tools. It is a way of working that makes infrastructure, releases and troubleshooting more reliable for the people who depend on them.
- Understand what happens from developer code to a live application
- Automate work that is repeated across servers or environments
- Use logs, metrics and monitoring to investigate issues
- Document infrastructure and deployment steps so others can reproduce them
Learn Linux before automation
Build command-line confidence first so Docker, Jenkins, Ansible and Kubernetes become easier to understand and troubleshoot.
Learn cloud with security awareness
Understand IAM, least privilege, secure access, network boundaries, cost monitoring and responsible AWS use.
Learn containers before orchestration
Build single and multi-container applications with Docker and Compose before moving to Kubernetes deployment and scaling.
Learn one end-to-end workflow
Connect GitHub, Jenkins, Docker, Ansible, Kubernetes and AWS components in a final project that shows the full delivery path.
Use AI to understand systems faster, while validating every operational change.
AI can help with Bash scripts, log interpretation, YAML manifests, pipeline troubleshooting and documentation. The professional advantage comes from checking commands, understanding security implications and testing changes safely.
Use it to explain Linux commands, investigate log messages, draft Bash scripts and break down cloud troubleshooting tasks.
Use it to review Jenkinsfiles, Dockerfiles, Ansible playbooks and Kubernetes manifests for logic, readability and risks.
Use it to explore AWS architecture choices, service interactions, deployment options and incident-response approaches.
Use it to research official documentation, cloud services, DevOps patterns and current configuration guidance before implementation.
DevOps skills are the foundation. AI-ready workflows can help you investigate and automate more effectively.
The benefit is not copying commands. It is moving from a symptom to a safe, documented and testable solution with clearer reasoning.
Can manage infrastructure, but may spend more time researching commands, logs and configuration options.
Hands-on tool knowledge matters. Yet unfamiliar errors, YAML problems, AWS decisions and pipeline failures can take longer when each investigation starts from scratch.
- Researches commands, error messages and documentation one issue at a time
- May need more time to outline a safe troubleshooting plan
- Writes runbooks and configuration notes manually
- Can find it slower to compare alternative infrastructure approaches
Can combine operational knowledge with faster research, clearer automation and stronger review habits.
AI is useful only when you can assess risk, test output, understand security controls and make the final engineering decision yourself.
- Breaks issues into logs, services, network, permissions and deployment checks
- Explores documentation and configuration options more efficiently
- Uses AI to support scripts, manifests, tests and operational documentation
- Creates first drafts faster, then validates them in controlled environments
Learn how software moves from a repository to a running service.
The advanced program is arranged in the same sequence that makes practical sense in real environments: operating system, cloud, code repository, containers, automation, orchestration and delivery.
Linux & Network
Prepare the server, users, permissions, services, ports, logs and scripts.
Git & Cloud
Manage code in GitHub and build secure AWS resources for the workload.
Containerise
Package the application with Docker and define multi-service setups using Compose.
Automate Delivery
Use Jenkins pipelines and Ansible playbooks to create repeatable build and server tasks.
Deploy & Observe
Deploy to Kubernetes, configure services, inspect logs and monitor the deployed workload.
One ordered path from Linux commands to a complete CI/CD project.
The 12-month advanced syllabus is grouped into practical learning stages instead of week numbers. Each stage builds on the skills needed before it.
Linux Foundations & Server Administration
Core learning
- Linux and open-source concepts; Ubuntu, Debian, CentOS and Amazon Linux awareness
- VirtualBox, VMware, WSL and Linux on AWS EC2
- Filesystem, terminal, shell, commands, permissions, users, groups and package installation
- Text editing, environment variables, aliases, processes, services, logs, disk management, archive tools and Cron
Networking, SSH, Bash & YAML
Core learning
- IP addresses, public and private IP, DNS, ports, HTTP/HTTPS, TCP/UDP and firewall basics
- ping, curl, wget, netstat, ss, traceroute and nslookup
- SSH keys, secure remote access and connection troubleshooting
- Bash variables, conditions, loops, functions, arguments, error handling and YAML basics
Git & GitHub for Collaborative Delivery
Core learning
- Version control, repositories, commits, history, clone, pull, push, diff and tags
- Feature, release and hotfix branches; merge conflicts and rebase basics
- .gitignore, README writing, pull requests, code reviews, issues and branch protection awareness
Cloud Computing & AWS Foundations
Core learning
- IaaS, PaaS, SaaS, public/private/hybrid cloud, regions, availability zones, scalability and high availability
- Shared-responsibility model, AWS account basics, IAM users, roles, policies, MFA and least privilege
- AWS CLI, CloudShell, billing awareness, budgets and cost alerts
AWS Core Services, Networking & Monitoring
Core learning
- EC2, AMIs, instances, key pairs, security groups, Elastic IP, EBS, snapshots and scaling basics
- S3 buckets, versioning, lifecycle rules, policies, static hosting and AWS CLI file operations
- VPC, subnets, route tables, internet gateway, NAT concept, CIDR, network ACLs and CloudWatch metrics/logs/alarms
Docker & Multi-Container Applications
Core learning
- Containers vs virtual machines, Docker architecture, images, containers, Docker Hub, Dockerfiles, layers and logs
- Port mapping, environment variables, bind mounts, volumes, networking, image tagging and publishing
- Docker Compose, services, networks, volumes, health checks, restart policies, multi-stage builds and image optimisation
Jenkins & CI/CD Pipelines
Core learning
- Continuous Integration, Continuous Delivery and Continuous Deployment
- Jenkins installation, Docker-based Jenkins, dashboard, plugins, credentials, agents and build logs
- Freestyle jobs, declarative pipelines, Jenkinsfiles, pipeline stages, GitHub integration and troubleshooting
Ansible & Configuration Automation
Core learning
- Agentless automation, control nodes, managed nodes, inventories, ad hoc commands and modules
- Playbooks, YAML, variables, facts, conditions, loops, handlers, templates, roles and idempotency
- Ansible Vault, Ansible Galaxy, SSH-based automation and error handling
Kubernetes Deployment, Networking & Storage
Core learning
- Clusters, control plane, worker nodes, kubectl, Minikube/Kind, pods, deployments, replicas, labels, namespaces and manifests
- Rolling updates, rollbacks, logs, debugging, services, ingress basics, ConfigMaps, Secrets and environment variables
- Persistent volumes, claims, storage classes, probes, resources, autoscaling, RBAC awareness, Helm basics and troubleshooting
End-to-End Cloud & DevOps Capstone
Core learning
- Complete flow: developer code → GitHub → Jenkins → build/test → Docker image → registry → Kubernetes deployment
- AWS components: IAM, EC2, S3, VPC, security groups and CloudWatch; ECR/EKS overview where relevant
- Linux scripts, Jenkinsfile, Dockerfile, Docker Compose, Ansible playbook, Kubernetes manifests, configuration and documentation
Start with cloud foundations or build the complete DevOps stack.
The 6-month program focuses on Linux and AWS. The 12-month program adds the complete automation and container-orchestration path for learners who want broader DevOps capability.
6-Month Training Program
Build hands-on confidence with Linux administration, networking, Bash scripting, cloud fundamentals and core AWS services for practical cloud operations.
- Linux fundamentals, administration, file permissions, services and logs
- Networking, SSH, Bash scripting, Cron and YAML basics
- Cloud fundamentals, IAM, AWS CLI and cost-awareness practices
- AWS EC2, S3, VPC, security groups and CloudWatch
- 6-month internship and project-work pathway after training
Ask About the 6-Month Program
Internship opportunities, project work, work-experience documentation and job recommendations depend on progress, eligibility, completion requirements and available opportunities.
12-Month Training Program
Build a complete cloud and DevOps toolkit: Linux, AWS, Git/GitHub, Docker, Jenkins, Ansible, Kubernetes and an end-to-end CI/CD capstone.
- Everything included in the 6-month Linux and AWS program
- Git/GitHub, Docker, Docker Compose and image publishing
- Jenkins CI/CD pipelines and Ansible configuration automation
- Kubernetes deployment, networking, storage and troubleshooting
- 12-month internship and project-work pathway after training
Ask About the 12-Month Program
Internship opportunities, project work, work-experience documentation and job recommendations depend on progress, eligibility, completion requirements and available opportunities.
Build infrastructure proof, not only tool knowledge.
Each project gives you a practical story to explain in a portfolio, technical discussion or interview.
Server Health Monitor
Create a Bash-based monitoring script that checks services, CPU, memory, disk, connectivity and logs on a schedule.
Secure Cloud Setup
Build a VPC, public subnet, EC2 server, Nginx page, S3 backup and CloudWatch alarm with documented configuration.
Containerised Application
Package a frontend, backend or API with Docker, then use Compose for a multi-service setup with persistent data.
Automated Delivery Flow
Build a pipeline that pulls code, tests, creates an image and uses automation to configure and deploy application services.
CI/CD DevOps Capstone
Deploy a containerised web application with manifests, Services, ConfigMaps, Secrets, monitoring checks and clear documentation.
Support that helps you practise, deploy and present your work.
Every benefit is designed to support regular technical practice, strong project proof and clearer career preparation.
Practical Cloud Labs
Work with Linux servers, AWS resources, logs, scripts, Docker, pipelines and Kubernetes deployments.
Project Completion Certificate
Document practical cloud and DevOps projects completed during the training journey.
GitHub Portfolio Guidance
Organise repositories, READMEs, scripts, manifests and architecture diagrams clearly.
Notes & Video References
Use class notes, learning references and revision resources beyond the live sessions.
Resume Templates
Present Linux, AWS, Docker, CI/CD, infrastructure automation and project work with clarity.
Interview Preparation
Prepare to explain services, scripts, pipelines, deployment choices and troubleshooting work.
Industry Exposure
Understand deployment workflows, change control, logs, monitoring and operational responsibility.
Internship Opportunities
Explore practical exposure after successful progress, program completion and available opportunities.
Job Recommendations
Receive role guidance and job recommendations based on readiness and available opportunities.
Learn cloud and DevOps from anywhere. Practise with real workflows.
Live online sessions make this Linux, Cloud and DevOps training program accessible across India. Learners who prefer classroom learning can also join offline sessions in Agra.
Learn from home, college or your current city.
Join live sessions, practise commands and cloud tasks, build repositories and complete guided project work without needing to relocate.
- Live interactive Linux, AWS and DevOps sessions
- Guided terminal, cloud and deployment practice
- Project-based assignments and review support
- Suitable for students, job seekers and working learners
Choose a classroom environment for in-person technical practice.
Attend sessions at Learn2Earn Labs, work through infrastructure challenges with mentors and learn alongside focused learners.
- Face-to-face mentorship and doubt resolution
- Structured classroom practice environment
- Hands-on Linux, cloud and pipeline tasks
- Local career guidance and project support
Build options beyond a certificate.
As your Linux, cloud, automation and deployment skills grow, you can prepare for entry-level infrastructure, cloud support and DevOps opportunities.
Get clear before you start.
Who can join this Linux, Cloud and DevOps training program?
This program is suitable for college students, graduates, job seekers, career switchers and working professionals who want to build practical cloud and DevOps skills. No prior Linux or AWS knowledge is required, but regular hands-on practice is essential.
What is covered in the 6-month program?
The 6-month program focuses on Linux, Linux administration, networking, Bash scripting, cloud fundamentals and AWS core services such as IAM, EC2, S3, VPC, security groups and CloudWatch.
What extra topics are included in the 12-month program?
The 12-month program includes the full Linux and AWS foundation plus Git/GitHub, Docker, Docker Compose, Jenkins CI/CD, Ansible configuration automation, Kubernetes and an end-to-end DevOps capstone.
Can I join from another city?
Yes. Live online training is designed for learners across India. You can join from home, college, hostel or your current city. Offline learning is also available for learners who want to attend at the Agra centre.
Which AI tools will I learn to use?
You will use ChatGPT, Claude, Gemini and Perplexity for command explanation, Bash scripting, YAML review, log interpretation, pipeline troubleshooting, architecture research and documentation. You will learn to validate all output before using it.
Will I work on practical projects?
Yes. You will work on Linux health monitoring, AWS setup, Dockerised applications, CI/CD pipelines, Ansible automation, Kubernetes deployment and a complete DevOps capstone according to your program path.
Are internship opportunities and career support available?
Yes. The program includes internship opportunities, project work, work-experience guidance, resume templates, interview preparation and job recommendations. These depend on progress, eligibility, completion requirements and available opportunities.
Is a job guaranteed after completing the program?
No responsible training provider can guarantee a job. This program is designed to improve readiness through practical Linux, cloud, automation and deployment skills, project work, AI-assisted workflows and career preparation.
⭕ Take the next step
Build the cloud and DevOps skills that you can prove through real projects.
Get clear guidance on the right program, learning mode, curriculum, fee, current batch availability and the best starting point for your current level.