Class Curriculum

Introduction to DevOps

The Complete Journey: From Beginner to Production-Ready DevOps Engineer

Duration: 72 classes - 36 weeks (2 sessions per week)

Learning Objectives

Course-Wide Competencies:

Upon successful completion of this comprehensive program, you will be able to:

  • Master the complete DevOps toolchain from Linux fundamentals to production Kubernetes deployments

  • Build and maintain fully automated CI/CD pipelines that integrate testing, security, and deployment

  • Design scalable, secure, and cost-effective cloud-native applications using modern DevOps practices

  • Implement infrastructure as code solutions for reproducible and maintainable deployments

  • Apply industry best practices for collaboration, documentation, and continuous improvement

  • Demonstrate professional readiness for DevOps, SRE, and cloud engineering roles

The DevOps Learning Path:

This curriculum follows a carefully crafted progression from foundational concepts to advanced production practices:

  1. Foundation Layer (Weeks 1-10): Master the essential building blocks

  2. Automation Layer (Weeks 11-19): Build automated workflows and pipelines

  3. Containerization Layer (Weeks 20-28): Package and orchestrate applications

  4. Cloud Layer (Weeks 29-35): Deploy to production cloud environments

  5. Mastery Layer (Week 36): Integrate everything in a capstone project

Module 0: DevOps Concepts and Tools

Week 1: Introduction to DevOps

The DevOps Mindset and Cultural Foundation

Learning Goals:

  • Understand the cultural and technical transformation that DevOps brings to organizations

  • Recognize how DevOps addresses traditional silos between development and operations

  • Identify the business impact of DevOps practices on delivery speed and quality

Content:

  • DevOps history, principles, and cultural impact

  • Traditional IT challenges vs. DevOps solutions

  • The T-shaped skillset: broad knowledge with deep specialization

  • Overview of the complete DevOps toolchain and career paths

  • Setting expectations for continuous learning and collaboration

Hands-on Lab:

  • Team formation and collaborative workspace setup

  • Introduction to course project and learning methodology

Module 1: Foundation Layer - Systems and Infrastructure Fundamentals

Weeks 2-6: Environments and Linux Mastery

From Physical Servers to Cloud-Native Infrastructure

Learning Goals:

  • Master Linux as the foundation of modern DevOps infrastructure

  • Understand the evolution from bare metal to containers to cloud

  • Build confidence with command-line tools used daily by DevOps professionals

Content:

  • Computing environments: bare metal → VMs → containers → cloud

  • Linux fundamentals: file systems, permissions, process management

  • Command-line productivity and automation

  • System monitoring, logging, and troubleshooting

  • Security fundamentals and access management

Practical Projects:

  • Set up and secure a Linux development environment

  • Build system monitoring and alerting scripts

  • Create user management and security hardening procedures

Weeks 7-8: Shell Scripting

Automation Foundation with Shell Scripts

Learning Goals:

  • Transform manual tasks into automated, reliable scripts

  • Build reusable tools that improve team productivity

  • Establish foundations for larger automation frameworks

Content:

  • Bash scripting best practices and error handling

  • Building command-line utilities and system tools

  • Integration with system services and cron jobs

  • Script testing, debugging, and maintenance

Practical Projects:

  • Create deployment automation scripts

  • Build system backup and maintenance tools

  • Develop monitoring and alerting utilities

Weeks 9-10: Advanced Linux and Networking Basics

Production System Administration

Learning Goals:

  • Manage production Linux systems with confidence

  • Understand networking fundamentals for containerized and cloud environments

  • Apply security and performance optimization techniques

Content:

  • Advanced system administration and performance tuning

  • Network configuration, firewalls, and security

  • Service management with systemd

  • Log analysis and troubleshooting methodologies

Practical Projects:

  • Configure and optimize a web server environment

  • Implement network security and monitoring

  • Build comprehensive system health checks

Module 2: Automation Layer - Programming and Pipeline Fundamentals

Weeks 11-14: Python for DevOps

Building DevOps Tools and Automation

Learning Goals:

  • Create powerful automation tools that replace manual processes

  • Build APIs and command-line utilities for infrastructure management

  • Integrate Python with existing DevOps toolchains

Content:

  • Python fundamentals for infrastructure automation

  • API development for configuration and monitoring

  • Command-line tool creation and distribution

  • Data processing for logs, metrics, and system information

  • Testing and quality assurance for automation code

Practical Projects:

  • Build a CLI tool for system management

  • Create APIs for application configuration

  • Develop log analysis and reporting tools

Weeks 15: Git and GitHub

Collaborative Development and Version Control

Learning Goals:

  • Master professional Git workflows used in DevOps teams

  • Implement branching strategies that support continuous delivery

  • Integrate version control with automation and deployment processes

Content:

  • Advanced Git workflows (GitFlow, GitHub Flow, trunk-based development)

  • Code review processes and collaborative development

  • Git hooks for automation and quality gates

  • Repository management and security best practices

Practical Projects:

  • Implement team branching strategy and code review process

  • Create Git hooks for automated testing and quality checks

  • Set up collaborative development workflow

Week 16: Documentation as Code (DocOps)

Living Documentation and Knowledge Management

Learning Goals:

  • Create documentation that evolves with code and infrastructure

  • Build automated documentation generation and deployment

  • Establish documentation culture and processes

Content:

  • Documentation-as-code principles and toolchains

  • Automated documentation generation from code and APIs

  • Collaborative documentation workflows

  • Integration with CI/CD for documentation deployment

Practical Projects:

  • Set up automated documentation pipeline

  • Create comprehensive project documentation

  • Implement documentation quality gates

Weeks 17-18: CI/CD Pipelines

Automated Build, Test, and Deployment

Learning Goals:

  • Build production-ready CI/CD pipelines that ensure quality and reliability

  • Implement security, testing, and compliance automation

  • Design multi-environment deployment strategies

Content:

  • GitHub Actions and pipeline-as-code

  • Automated testing strategies (unit, integration, security)

  • Multi-environment deployment patterns

  • Pipeline security and secret management

  • Monitoring and observability for deployments

Practical Projects:

  • Build complete CI/CD pipeline for Python application

  • Implement automated security scanning and quality gates

  • Create multi-environment deployment workflow

Week 19: Mid-term Project and Retrospective

Integration and Reflection

Content:

  • Complete automation project integrating all learned tools

  • Team presentations and peer feedback

  • Skills assessment and learning path adjustment

  • Industry career guidance and networking

Module 3: Containerization Layer - Modern Application Packaging and Orchestration

Weeks 20-22: Containers and Docker

From “Works on My Machine” to Production Consistency

Learning Goals:

  • Eliminate environment inconsistencies through containerization

  • Build production-ready container images with security and performance optimization

  • Orchestrate multi-service applications using modern container tools

Content:

  • Container fundamentals and the shift from VMs

  • Docker and Podman for container creation and management

  • Production Dockerfile best practices and security

  • Multi-container applications with Docker Compose

  • Container registries and image management

  • Security scanning and vulnerability management

Practical Projects:

  • Containerize the Python application from previous modules

  • Build secure, optimized container images

  • Create multi-service application with databases and caching

Weeks 23-25: Kubernetes and Container Orchestration

Production Container Orchestration at Scale

Learning Goals:

  • Deploy and manage applications in production Kubernetes clusters

  • Implement zero-downtime deployments with automatic scaling

  • Integrate Kubernetes with CI/CD pipelines for automated deployments

Content:

  • Kubernetes architecture and core concepts

  • Pods, Services, Deployments, and ConfigMaps

  • Production deployments with rolling updates and rollbacks

  • Horizontal Pod Autoscaling and resource management

  • CI/CD integration with Kubernetes

  • Monitoring and troubleshooting applications

Practical Projects:

  • Deploy applications to local and cloud Kubernetes clusters

  • Implement automated CI/CD pipeline to Kubernetes

  • Build production-ready deployments with scaling and monitoring

Weeks 26-28: Helm and Advanced Kubernetes

Package Management and GitOps for Kubernetes

Learning Goals:

  • Package applications for reusable, templated deployments

  • Implement GitOps workflows for automated infrastructure management

  • Apply production best practices for security, monitoring, and cost optimization

Content:

  • Helm for Kubernetes package management

  • Creating reusable Helm charts for multiple environments

  • ArgoCD and GitOps workflows

  • Production security, monitoring, and cost optimization

  • Kubernetes operators and advanced patterns

Practical Projects:

  • Convert applications to Helm charts with environment-specific configurations

  • Implement complete GitOps workflow with ArgoCD

  • Apply production security and monitoring best practices

Module 4: Cloud Layer - Production Infrastructure and Deployment

Weeks 29-30: Cloud Fundamentals

From On-Premises to Cloud-Native Architecture

Learning Goals:

  • Understand cloud service models and choose appropriate solutions

  • Deploy applications to major cloud platforms with native tools

  • Implement cloud security and cost optimization best practices

Content:

  • Cloud computing models: IaaS, PaaS, SaaS

  • AWS, Azure, and GCP core services comparison

  • Cloud-native application patterns and architectures

  • Security, compliance, and cost management

  • Multi-cloud and hybrid strategies

Practical Projects:

  • Deploy applications to multiple cloud providers

  • Implement cloud security and monitoring

  • Build cost-effective, scalable cloud architectures

Weeks 31-35: Infrastructure as Code with Terraform and Ansible

Automated Infrastructure Provisioning and Configuration

Learning Goals:

  • Automate infrastructure provisioning across multiple cloud providers

  • Implement configuration management for consistent, reproducible deployments

  • Build comprehensive infrastructure automation that integrates with CI/CD

Content:

  • Infrastructure as Code principles and best practices

  • Terraform for multi-cloud infrastructure provisioning

  • Ansible for configuration management and application deployment

  • State management, modules, and reusable infrastructure patterns

  • Integration with CI/CD pipelines for infrastructure automation

  • Security, compliance, and governance for infrastructure code

Practical Projects:

  • Build complete cloud infrastructure using Terraform

  • Automate application deployment and configuration with Ansible

  • Create reusable infrastructure modules and patterns

  • Implement infrastructure CI/CD with testing and validation

Week 36: Integration Project and Career Preparation

Capstone Project and Professional Development

Learning Goals:

  • Demonstrate mastery by building a complete, production-ready DevOps solution

  • Present technical solutions effectively to stakeholders

  • Prepare for DevOps career opportunities and continued learning

Content:

  • Comprehensive capstone project integrating all course concepts

  • Industry best practices and real-world case studies

  • Career guidance: resume building, interview preparation, networking

  • Advanced topics and continuous learning pathways

  • Project presentations and peer feedback

  • Course completion and certification

Capstone Project Requirements:

  • Multi-tier application (web, API, database) deployed to cloud

  • Complete CI/CD pipeline with automated testing and deployment

  • Infrastructure as code with multiple environments

  • Security, monitoring, and logging implementation

  • Comprehensive documentation and runbooks

  • Team collaboration and code review processes