Become a DevOps Engineer Who Won’t Be Replaced by AI
Hands-on work with real infrastructure, mentorship from active DevOps engineers, and support at every stage—everything you need for a confident, structured start in DevOps and a cloud career.
About the Program
On-Demand Tech Stack
Learn exactly what employers are looking for today — nothing extra, only practical, job-ready skills.
Real-World Experience
Our industry experts teach you how things work in practice, not just in theory or textbooks.
Practice-Driven Learning
Every module includes hands-on labs and mini-projects. By the end, you’ll build and present a capstone project for your portfolio.
Career Support
We don’t just teach programming — we guide you through job search strategies, best practices, and support you every step of the way.
Convenient Format
Evening classes and a fully remote format make it easy to combine learning with your job, family, or other responsibilities.
Supportive Community
You’re not alone. Our team of mentors, tech assistants, and fellow students will support you both technically and emotionally — to stay motivated, focused, and inspired.
Built for Careers at Top Tech Companies
Average DevOps Engineer Salaries in the U.S.
Who is Our Bootcamp for
Those taking their first steps in IT
You don’t need to be a programmer or an experienced system administrator. We’ll guide you step by step into the world of DevOps.
IT professionals looking to transition into DevOps
Already working in IT and ready for the next level? This course helps you structure your knowledge, adopt the DevOps mindset, and move into a more in-demand, higher-paying role.
Graduates and junior specialists
Have theory but little understanding of how things work in production? We’ll bridge the gap between academic knowledge and real-world infrastructure.
Curriculum Overview
We’ve designed this bootcamp to be intensive, yet perfectly paced for beginners. Every week, you’ll receive theoretical lessons on the platform, get clear explanations from experts during live classes, and join live coding and code-along sessions. You will also put your knowledge into action during pair programming labs — because we believe the best way to learn is by doing.
- What DevOps is and how the profession works. You’ll understand the role of a DevOps engineer, their responsibilities, and their place between development and infrastructure—and why DevOps is critical for business.
- The application lifecycle—from code to production. Learn how an application moves from source code to production, and where servers, pipelines, containers, and automation come into play.
- Linux and the command line basics. Gain confidence working with servers: running commands, connecting via SSH, and operating without relying on graphical interfaces.
- Git and GitHub (foundations). Understand how code is stored and managed, how repositories, commits, and branches work—the foundation of CI/CD and real-world team collaboration.
- How CI/CD and automation work (conceptually). Learn what pipelines are, why they matter, and how DevOps eliminates manual, repetitive work for teams.
- Clouds and infrastructure (overview). Get a basic understanding of cloud computing, why AWS is used, and how applications live and run in cloud environments.
- AI as a DevOps engineer’s tool. Learn how to use AI for learning, generating configurations, explaining commands, finding errors, and speeding up your workflow—from the very beginning of the course.
- A clear understanding of the DevOps profession and what you’ll be learning next
- A solid technical picture: code → server → automation → deployment
- A fully configured working environment for the entire course
- Confidence before starting the core training (no “I don’t understand anything” feeling)
- Readiness to work with real DevOps tools and AI starting from the first module
- Working in the Linux terminal. Learn how to manage servers without a graphical interface: run commands, configure the system, and work efficiently from the command line.
- Core system commands. Navigate the file system, manage files and directories, view logs, and diagnose system issues.
- SSH and remote server access. Connect to real servers securely and work with them in a production-like environment.
- Processes, users, and permissions. Understand which services are running, who has access to the system, and how security is enforced.
- Server environment setup. Install required packages, perform basic system configuration, and prepare servers for further automation and deployment.
- Using AI in Linux workflows. Learn how to use AI to interpret terminal errors, generate and improve bash scripts, speed up learning, and reduce manual googling.
- Confident command of Linux as a core DevOps tool
- Practical skills for working with servers via terminal and SSH
- A solid understanding of system internals, processes, and permissions
- A ready-to-use server environment for upcoming modules (Docker, CI/CD, AWS)
- The ability to use AI as a day-to-day assistant in DevOps work
- Git fundamentals. Understand how code is stored and changed over time: commits, version history, and source control.
- Branches and branching workflows. Learn how to work in parallel, experiment safely, and introduce changes without risking production stability.
- Pull requests and team collaboration. Gain experience proposing changes, going through code reviews, and working by the same rules used in real engineering teams.
- GitHub as a DevOps work tool. Use repositories, issues, pull requests, and code reviews, and integrate GitHub with CI/CD pipelines.
- AI-assisted Git workflows. Learn how to use AI for code and pull request reviews, generating README files, analyzing changes, identifying potential issues, and automating git hooks and routine tasks.
- Confident, industry-standard use of Git and GitHub
- A solid understanding of team workflows and code review practices
- Experience with Git-flow, commonly expected in interviews
- A clean, well-structured repository ready for CI/CD
- The ability to use AI as an assistant for code and documentation management
- What containerization is and why it’s needed. Understand how DevOps solves the “it works on my machine but not on the server” problem—and why Docker has become an industry standard.
- Creating a Dockerfile. Learn how to define an application environment: dependencies, configuration, build steps, and startup commands.
- Images and containers. Understand the difference between images and containers, manage container lifecycles, and control application versions.
- Volumes and networks. Learn how to persist data outside containers, configure networking between services, and prepare for microservices architectures.
- Building and running a real application in Docker. Containerize an application from scratch and bring it to a fully working state.
- AI-assisted Docker workflows. Use AI to generate Dockerfiles, optimize images, and speed up troubleshooting.
- A clear understanding of containerization as a core DevOps tool
- Practical skills for creating and running Docker containers for real applications
- Hands-on experience with Docker volumes and networks
- A fully containerized application ready for the next modules (CI/CD, AWS)
- The ability to use AI to accelerate Docker workflows and resolve issues
- What CI/CD is and why it matters. Understand how automation removes manual deployments, reduces errors, and accelerates product delivery.
- CI/CD pipeline architecture. Learn how build, test, and deploy stages are structured and how they connect into a single workflow.
- CI/CD pipeline setup. Create working pipelines in GitHub Actions and GitLab CI for real-world projects.
- Automated build and testing. Ensure that every code change is automatically built and tested without human intervention.
- Automated application deployment. Configure deployment to staging or production environments as part of the pipeline.
- Working with YAML configurations. Read, write, and understand CI/CD configs as infrastructure as code.
- AI-assisted CI/CD workflows. Use AI to generate YAML files, debug failed pipeline steps, and optimize workflows.
- A clear understanding of CI/CD as a core DevOps practice
- Practical skills in building and maintaining working pipelines
- The ability to automate builds, tests, and deployments
- A production-ready CI/CD pipeline integrated with Docker and GitHub
- Skills to use AI for faster pipeline setup and ongoing maintenance
- What cloud computing is and why it’s needed. Understand how modern applications are deployed and scaled without owning physical servers.
- EC2 — compute resources. Launch and configure virtual servers, manage access, and control the lifecycle of instances.
- S3 — object storage. Store files, backups, build artifacts, and static assets.
- IAM — access and security management. Configure users, roles, and access policies following the principle of least privilege.
- Deploying applications in AWS. Deploy containerized applications in the cloud with basic architecture and security considerations.
- Basic cloud architecture. Understand how AWS services are connected and what a typical production setup looks like.
- AI-assisted AWS workflows. Use AI to generate IAM policies, design architecture diagrams, and identify potential security gaps.
- A solid understanding of cloud principles using AWS as an example
- Hands-on experience with key services: EC2, S3, and IAM
- The ability to deploy applications in the cloud
- A foundational understanding of cloud architecture and security
- Skills to use AI as an assistant when designing and optimizing cloud infrastructure
- Infrastructure as Code (IaC). Learn how to describe infrastructure as code instead of configuring it manually.
- Terraform fundamentals. Understand project structure, providers, resources, variables, and outputs.
- Automated AWS infrastructure provisioning. Deploy servers, networks, storage, and other AWS resources using Terraform.
- State management. Learn how Terraform tracks infrastructure, why the state file is critical, and how to avoid conflicts and common mistakes.
- Terraform modules. Build reusable infrastructure components, scale projects, and simplify long-term maintenance.
- Provisioners and resource lifecycle. Understand when and how to use provisioners and follow best practices for managing resource lifecycles.
- AI-assisted Terraform workflows. Use AI to generate .tf files and create and optimize Terraform modules.
- A strong understanding of IaC as the foundation of modern DevOps infrastructure
- Practical skills for provisioning AWS infrastructure automatically with Terraform
- The ability to work with Terraform state safely and confidently
- Production-ready Terraform configurations for your portfolio
- Skills to use AI to speed up and simplify infrastructure-as-code development
- Why monitoring matters in DevOps. Understand system health, detect issues before users notice them, and keep services stable and reliable.
- Prometheus — metrics collection. Set up metrics collection from services and infrastructure, and learn which metrics actually matter.
- Grafana — data visualization. Build clear, actionable dashboards to analyze system health and support decision-making.
- Metrics and alerts. Define key metrics, set thresholds, and configure alerts to detect problems early.
- Logging and log analysis. Collect, store, and analyze logs to identify errors and system bottlenecks.
- Incident response. Learn how DevOps engineers use monitoring and logs to diagnose issues and restore services.
- AI-assisted monitoring and logging. Use AI to analyze logs, automatically detect anomalies in metrics, generate and improve Grafana dashboards, and find correlations between metrics and logs.
- A clear understanding of monitoring as a core pillar of stable infrastructure
- Hands-on experience with Prometheus and Grafana
- The ability to configure metrics, alerts, and basic incident response workflows
- Ready-to-use dashboards and monitoring configurations for your portfolio
- Skills to use AI to speed up incident analysis and improve system observability
- Build a complete DevOps pipeline. Combine all the skills from the course into a single end-to-end project that mirrors real production DevOps workflows.
- Containerize an application with Docker. Package an application into containers so it runs consistently across different environments.
- Set up CI/CD automation. Configure automated build, testing, and deployment triggered by every code change.
- Deploy to the cloud (AWS). Run your application and infrastructure in a real cloud environment using industry best practices.
- Manage infrastructure as code. Provision servers, networks, and services using Terraform instead of manual configuration.
- Add monitoring and logging. Collect metrics, configure alerts, and visualize system health to ensure system reliability.
- Use AI throughout the workflow. Accelerate configuration, debugging, documentation, and analysis with AI tools.
- A complete, industry-aligned DevOps project
- Detailed technical documentation describing architecture and design decisions
- A reusable set of AI prompts applicable to real DevOps work
- A GitHub repository ready to showcase to employers
- Clear, well-structured documentation enhanced with AI
Meet Our Team
Anastasiia
Switched from being a medical doctor to software development in just one year. Three years later, she became a team lead — now she helps others follow the same path.
Angelina
Always there to keep you updated and supported behind the scenes — she makes sure everything runs smoothly.
Kate
6 years in IT education. She built this course to include only what truly matters — practical knowledge that delivers real results.
Anastasia
Switched from being a medical doctor to software development in just one year. Three years later, she became a team lead — now she helps others follow the same path.
Angelina
Always there to keep you updated and supported behind the scenes — she makes sure everything runs smoothly.
Built for Careers at Top Tech Companies
Your Outcome After Graduation
By the time you graduate, you’ll have a strong portfolio packed with real-world personal and team projects — a powerful tool to impress future employers. You’ll feel confident using the technologies and workflows you’ve mastered throughout the bootcamp. Plus, our career coaches and HR specialists will support you every step of the way — from crafting a standout resume and LinkedIn profile to preparing for interviews and navigating your job search.
Start your journey into tech right now
- Full access to all course materials and platform
- 3 live workshops per week with instructor
- Practical projects, code reviews, and homework with feedback"
- Interview preparation and mock interviews
- Personalized career consultations
- Access to extra modules (algorithms, interview prep, onboarding)
- Career support for 3 months after course
- Private community of alumni and experts