DevOps & Cloud Course - AWS, Docker, Kubernetes with AI
This 12-week course takes you from Linux fundamentals to designing production-grade cloud architectures. Learn AWS, Docker, Kubernetes, Terraform, Ansible, CI/CD with Jenkins and GitHub Actions, and monitoring with Prometheus and Grafana through live, mentor-led sessions. Build 9 hands-on projects and earn dual IBM & GeeksforGeeks certification.
Course Overview
IBM & GeeksforGeeks Certification
Earn industry-recognized certification
Modern AI Toolkit
Work with the latest AI tools and technologies
Live Soft Skills Sessions
Biweekly expert-led sessions for interview success
Skills You'll Gain
Learn and master key DevOps and Cloud concepts.
Linux System Administration
Shell Scripting & Automation
Python Automation
AWS Cloud Infrastructure
Networking & Cloud Security
Git & Version Control
Docker Containerization
CI/CD Pipeline Development
Kubernetes Orchestration
Cloud-Native Application Deployment
Infrastructure as Code (IaC)
Monitoring, Observability & MLOps
Technologies You'll Learn
Master industry-standard DevOps and cloud engineering tools and technologies.
LinuxBashPythonGit & GitHubAWSDocker
Jenkins
GitHub Actions
SonarQubeKubernetes
Helm
Ansible
Terraform
Prometheus
Grafana
Projects You'll Build
Real-world projects to apply your skills
Automate filesystem tasks, monitor CPU, memory, and disk usage, and generate system performance reports with alerts.
Build a Python CLI tool to analyze logs, automate routine operations, and generate reports, with the project version-controlled on GitHub.
Configure and harden EC2 instances, establish secure access controls, and build a reliable baseline for production cloud workloads.
Containerize an AI inference service using FastAPI or TorchServe and document image usage and operational procedures.
Connect a GitHub Actions self-hosted runner with Jenkins to trigger automated jobs and streamline CI/CD workflows.
Deploy a microservices application on Amazon EKS with health checks, persistent volumes, and resilient application workloads.
Deploy an EKS application with TLS security, Nginx Ingress, and automated certificate management while evaluating monolithic versus microservices architectures.
Automate multi-node Kubernetes cluster setup using repeatable, idempotent Ansible playbooks and consistent configuration management.
Build observability dashboards to track ML model latency, accuracy, and error rates and identify performance issues in deployed models.
Course Syllabus
Session 1: Linux Fundamentals I
- Linux filesystem, kernel, shell, and the FHS
- Permissions model (rwx, octal, chmod/chown)
- Process management (ps, top, signals, fg/bg)
- Pipes and redirection basics
Session 2: Linux Fundamentals II - Power User
- Text processing: grep, sed, awk one-liners
- Finding things: find, locate, which
- Users, groups, and sudo; packages (apt/dnf)
- Services with systemd; disk and memory tools
Session 3: Shell Scripting
- Shebang, variables, and quoting rules
- Conditionals: if / case, test operators
- Loops: for, while read, until
- Functions, arguments, and exit codes
Session 4: Shell Scripting: Filesystems & Performance
- Automate filesystem tasks
- Create monitoring scripts for CPU, memory, and disk usage with alerting mechanisms
- Learn defensive scripting techniques
Project: Make a project that combines filesystem automation and system performance reporting.
