Programming Languages
Cloud DevOps with AI Automation Course
6 Months
Advanced
Certified Course
Contact Us
+918160529351
info.nodetolearn@gmail.com
Surat, Gujarat
Course Overview
Cloud DevOps is the skill set behind every app that stays online 24/7, deploys updates without downtime, and scales automatically when traffic spikes. Companies today don't need people who just know server theory — they need engineers who can actually build deployment pipelines, manage containers, set up Kubernetes clusters, and automate the boring (but critical) ops work. That's exactly what this course trains you to do.
At NodeToLearn, you work hands-on from day one — no slideshows, no batch lectures, no waiting for others to catch up. This is a 1-on-1 mentorship program, so your sessions move at your pace. You'll start with real Linux server administration, move through Docker and Kubernetes, build CI/CD pipelines using Git and Jenkins, and finish by integrating AI tools that automate monitoring and script writing. Every concept is practiced live on a workstation, not just talked about.
The course also has a practical AI layer — you'll use tools like ChatGPT and AI-driven log analyzers to write automation scripts faster, catch server errors before they cause downtime, and generate monitoring reports automatically. This isn't a "future of DevOps" theory segment — you'll actually use these tools during the course.
By the time you finish, you'll have a real portfolio: a working CI/CD pipeline, a live-deployed application, and a telemetry dashboard — things you can show in interviews or freelance pitches.
At NodeToLearn, you work hands-on from day one — no slideshows, no batch lectures, no waiting for others to catch up. This is a 1-on-1 mentorship program, so your sessions move at your pace. You'll start with real Linux server administration, move through Docker and Kubernetes, build CI/CD pipelines using Git and Jenkins, and finish by integrating AI tools that automate monitoring and script writing. Every concept is practiced live on a workstation, not just talked about.
The course also has a practical AI layer — you'll use tools like ChatGPT and AI-driven log analyzers to write automation scripts faster, catch server errors before they cause downtime, and generate monitoring reports automatically. This isn't a "future of DevOps" theory segment — you'll actually use these tools during the course.
By the time you finish, you'll have a real portfolio: a working CI/CD pipeline, a live-deployed application, and a telemetry dashboard — things you can show in interviews or freelance pitches.
Who is this for?
This course is a strong fit for BCA, MCA, and B.Tech (IT/CS) students who want to move beyond coding assignments and into real infrastructure work. Backend developers who already know some programming and want to level up into cloud and DevOps roles will also find it very practical. System administrators who currently manage servers manually and want to automate that work will get immediate value. Tech founders or freelancers building SaaS products who need to manage their own deployments without a dedicated ops team are also a good fit. If you have zero Linux experience, that's okay — Module 1 starts from the ground up.
Career Outcomes
- Completing this Cloud DevOps course opens doors to roles like Cloud DevOps Engineer
- Site Reliability Engineer (SRE)
- Infrastructure Engineer
- Build & Release Engineer
- and Container Operations Specialist. There's also strong demand for freelance cloud consultants who can set up and manage deployment pipelines for growing startups and agencies. Experienced DevOps engineers are among the higher-paid profiles in Indian IT right now —. The combination of Kubernetes
- CI/CD pipelines
- and AI automation skills covered here directly maps to what job descriptions in this space are actively asking for.
Course Curriculum
Linux System Administration, Security & Bash Scripting
- Setting up a production-style Linux server environment: SSH key login, environment variables, and essential CLI shortcuts
- Linux file system basics: navigating directories, setting file permissions with chmod and chown, and monitoring running processes
- Writing Bash shell scripts for automation: variables, loops, arrays, and handling errors using exit codes
- Building a simple companion web dashboard in HTML5 to display server stats and simulation reports
Git Version Control, Docker Containers & Multi-Container Setup
- Git branching and merging: tracking changes, resolving code conflicts, and working with remote repositories and hooks
- Docker fundamentals: how containers differ from virtual machines, working with base images, and writing multi-stage Dockerfiles
- Configuring Docker networking, mounting host directories as volumes, and passing environment variables into containers
- Docker Compose: connecting multiple containers (like app + database) and managing their startup order and dependencies
Kubernetes Architecture & Container Orchestration
- Setting up a Kubernetes cluster: understanding master and worker nodes, etcd for cluster state, and using kubectl commands
- Deploying applications using Pods, Deployments, and ReplicaSets — and understanding how Kubernetes self-heals when containers crash
- Kubernetes networking: ClusterIP for internal services, NodePort for external access, and setting up an Ingress controller
- Configuring liveness and readiness health probes so Kubernetes knows when to restart or pause a container automatically
CI/CD Pipelines & Live Cloud Deployment
- Setting up Jenkins as a CI server, connecting it to a Git repository via webhooks, and triggering automated builds on code push
- Writing multi-stage pipeline scripts that pull code, run tests, build artifacts, and push to a live server automatically
- Implementing rolling deployments and zero-downtime release strategies on cloud infrastructure
- Provisioning a cloud server (VPS/droplet), installing SSL certificates, setting directory permissions, and hosting a live application
AI-Powered Operations, Performance Monitoring & Portfolio
- Using AI tools (including ChatGPT-based prompting) to auto-generate shell scripts, parse server logs, and speed up routine DevOps tasks
- Setting up AI-assisted log monitoring to detect performance anomalies and trigger automated recovery actions before downtime hits
- Adding basic analytics tracking to the companion dashboard to measure real user interaction data
- Portfolio capstone: building and deploying a complete CI/CD infrastructure pipeline with a live telemetry dashboard, plus mock interview drills