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Decoding DevOps: A Complete Hands-On Path from Basics to Advanced Projects with AI

In-depth discussion with hands-on project implementation across most modules
Easy to understand, instructional video course format with structured modular progression
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This is a comprehensive DevOps course by Imran Teli, covering 32 sections and 378 lectures (about 63 hours). It progresses from Linux, networking, Git, and Vagrant fundamentals through AWS cloud services, CI/CD with Jenkins/GitHub Actions/GitLab, Python and Bash automation, Terraform and Ansible for infrastructure and configuration management, Prometheus/Grafana monitoring, Docker and Kubernetes with Helm, and a closing end-to-end GitOps project using ArgoCD. It also integrates AI-assisted tools such as GitHub Copilot and Amazon Q throughout. The course is a broad, project-driven learning path aimed at taking beginners to job-ready DevOps practice.
  • main points
  • unique insights
  • practical applications
  • key topics
  • key insights
  • learning outcomes

  • • main points

    • 1
      Extremely broad and well-sequenced curriculum that builds from foundations (Linux, networking) to advanced topics (GitOps, observability), with a clear layered structure
    • 2
      Multiple hands-on projects including VProfile multi-VM setup, AWS lift-and-shift, GCP multi-tier deployment, Terraform VPC automation, and a complete GitOps pipeline
    • 3
      Integrates current AI-assisted workflows (GitHub Copilot for scripting, Amazon Q for AWS tasks, AI-assisted Helm development), which is timely and relevant

  • • unique insights

    • 1
      Pairs AI coding assistants directly with core DevOps tasks (scripting, Helm chart authoring, AWS automation) rather than treating AI as a separate topic
    • 2
      Ties observability (Prometheus, Grafana, Loki, Alloy, Slack alerting) into the same learning path as CI/CD and Kubernetes, reflecting the idea that monitoring is integral to production DevOps

  • • practical applications

    • Learners gain a hands-on toolchain spanning cloud, IaC, CI/CD, containers, orchestration, and GitOps, along with a portfolio-style end-to-end project. The course is well suited for building job-oriented skills, though the syllabus is listed at a high level, so the depth of each module cannot be verified from the outline alone.

  • • key topics

    • 1
      Cloud infrastructure and automation on AWS and GCP, including IaC with Terraform and configuration management with Ansible
    • 2
      CI/CD pipeline design and implementation with Jenkins, GitHub Actions, and GitLab CI/CD
    • 3
      Containerization and orchestration with Docker, Kubernetes, and Helm, culminating in a GitOps deployment with ArgoCD

  • • key insights

    • 1
      Integration of AI coding assistants (GitHub Copilot, Amazon Q) into everyday DevOps scripting and cloud automation workflows
    • 2
      Single course covering the full DevOps lifecycle from Linux fundamentals through observability and GitOps, avoiding the need to stitch together multiple courses
    • 3
      Multiple cumulative projects (VProfile, lift-and-shift, GCP deployment, GitOps platform) that reinforce skills across layers

  • • learning outcomes

    • 1
      Build, deploy, and manage infrastructure on AWS and GCP using Terraform and Ansible
    • 2
      Design and implement CI/CD pipelines with Jenkins, GitHub Actions, and GitLab CI/CD, including Docker and Kubernetes deployments
    • 3
      Set up a GitOps workflow with ArgoCD, Helm, and Kubernetes, and implement monitoring and alerting with Prometheus, Grafana, and Loki
examples
tutorials
code samples
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fundamentals
advanced content
practical tips
best practices

“ Introduction: Why Learn DevOps Today

DevOps has become a core discipline for organizations that need to ship software quickly, reliably, and securely. Decoding DevOps – From Basics to Advanced Projects with AI is a complete learning path designed to turn beginners into job-ready DevOps engineers through hands-on practice. Rather than focusing on theory alone, the course emphasizes real-world deployments, cloud infrastructure, CI/CD pipelines, Kubernetes, GitOps, and observability, giving learners the practical experience that modern engineering teams expect.

“ Foundation Layer: Linux, Networking, and Scripting

The course starts with the essentials. Learners build a strong base in Linux fundamentals and server management, set up multi-VM environments with Vagrant, and understand core networking concepts. Configuration data formats such as YAML and JSON are covered alongside Bash scripting, including variables, conditions, loops, and automation of administrative tasks. Students also work through the VProfile project introduction and multi-VM setup, which prepares them for the more advanced projects that follow.

“ Source Control, CI/CD, and Build Automation

Continuous integration and continuous delivery are central to DevOps practice. The course teaches Git and GitHub for source control, then moves into build automation with Maven and Jenkins, including master/agent architecture, Nexus integration, and SonarQube code quality checks. Learners also build pipelines with GitHub Actions, including self-hosted runners and security scanning, and with GitLab CI/CD, covering stages, pipelines, and Docker integration. The result is a practical understanding of how automated build and deployment workflows are designed and maintained.

“ Cloud Infrastructure on AWS and GCP

Cloud skills are essential for modern DevOps roles. On AWS, the course covers IAM, EC2, EBS, ELB, S3, RDS, CloudWatch, Auto Scaling, Route 53, Systems Manager, Lambda, and Elastic Beanstalk. Learners complete a lift-and-shift migration project, then move on to re-architecting applications using PaaS and SaaS models and cloud-native design principles. The course also includes a multi-tier application deployment on Google Cloud Platform, featuring VPCs, firewall rules, managed instance groups, Cloud SQL, Memorystore, Cloud DNS, and HTTPS load balancers.

“ Infrastructure as Code and Configuration Management

Infrastructure as Code allows teams to define and version their environments reliably. Using Terraform, learners cover fundamentals, variables, modules, remote state, and backends, culminating in an AWS VPC automation project. For configuration management, Ansible is taught through ad hoc commands, modules, playbooks, variables, templates, handlers, and roles, with applications to cloud provisioning and deployment automation. Together, these tools give learners the ability to provision and configure infrastructure consistently at scale.

“ Containerization with Docker and Kubernetes

Containers are the standard unit of application packaging. The course explains Docker concepts including images, Dockerfiles, volumes, networks, and container best practices. It then introduces Kubernetes architecture, cluster setup, pods, deployments, services, ConfigMaps, secrets, ingress, and autoscaling. Helm is covered for packaging and managing Kubernetes applications, and a capstone project deploys the VProfile application to Kubernetes, addressing containerization, service exposure, scaling, and production deployment practices.

“ Monitoring and Observability in Production

A DevOps practice is incomplete without observability. The course teaches how to collect, visualize, analyze, and act on metrics and logs. Learners set up Prometheus and Grafana, use Loki for centralized logging, and deploy Alloy for metrics and log collection. Topics include PromQL fundamentals, dashboard design best practices, alerting and notification strategies, Slack integrations, and production monitoring practices that help teams detect and resolve issues quickly.

“ GitOps and Modern Cloud-Native Delivery

The course closes its technical journey with an end-to-end GitOps project. Learners build a deployment platform that uses GitHub Actions for CI, automated Docker image builds, container registry integration, and Helm-based deployments to Kubernetes. ArgoCD is installed and configured to implement GitOps workflows, where Git serves as the single source of truth, application updates are automated, and continuous deployment is handled declaratively. This project reflects the release management practices used by modern cloud-native teams.

“ AI-Assisted DevOps Workflows

Recognizing the growing role of AI in engineering productivity, the course integrates AI-powered tools throughout. GitHub Copilot assists with scripting in Bash, YAML, JSON, and Python, Amazon Q supports cloud automation and AWS tasks, and AI-integrated Helm workflows speed up chart development. These tools help learners automate faster, troubleshoot more efficiently, and boost overall productivity in real DevOps environments.

“ Who Should Take This Course and What You Will Achieve

The course is open to anyone who wants to learn DevOps, requiring only basic computer knowledge. By completing it, learners will be able to build and manage cloud infrastructure on AWS and GCP, implement Infrastructure as Code with Terraform, automate systems with Bash, Python, and Ansible, build CI/CD pipelines with Jenkins, GitHub Actions, and GitLab, containerize and orchestrate applications, implement observability, adopt GitOps with ArgoCD and Helm, and apply AI tools to DevOps work. The result is the practical skill set required for real-world DevOps and Cloud Engineering roles.

 Original link: https://www.udemy.com/course/decodingdevops/

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