DevOps + AI Automation Syllabus
Two tracks, twelve modules and 40+ hands-on topics. Start from Linux fundamentals and finish building autonomous AI agents for DevOps.
01Linux Foundations
- Basic Linux: file system, permissions, users and groups, processes, package management and the command line you will use every day.
02Azure DevOps
- Azure DevOps Introduction: the platform and how teams use it end to end.
- Azure Boards: work items, sprints and agile tracking.
- Azure Repos: Git repositories, branching and pull requests.
- Azure Pipelines (CI/CD): build and release automation.
03Git, GitHub & Jenkins
- Git & GitHub: version control workflows, collaboration and best practices.
- CI/CD with Jenkins: declarative pipelines, integrations and automated delivery.
04GitHub Actions
- GitHub Actions Fundamentals: events, jobs, runners and secrets.
- Building Workflows: real build, test and deploy pipelines.
- Advanced GitHub Actions: reusable workflows, matrices and optimisation.
05Compute & Containers
- Virtual Machines: provisioning, configuration and management.
- Docker: images, containers, registries and multi-stage builds.
- Kubernetes: pods, deployments, services, scaling and orchestration.
06Infrastructure as Code
- Terraform: providers, state, modules and provisioning real infrastructure.
- Terraform + Ansible Integration: provisioning and configuration in one workflow.
- Ansible: playbooks, roles, inventories and configuration management.
07Automation Scripting
- Bash Scripting: automate routine operations and glue tools together.
- Python for DevOps: scripting, APIs and automation beyond the shell.
08Monitoring & Observability
- Monitoring Fundamentals: metrics, logs, alerts and SLOs.
- Prometheus: collecting and querying metrics.
- Grafana: dashboards and visual observability.
09Prompt Engineering
- Core Prompting Techniques: patterns that get reliable results from LLMs.
- Prompt Engineering for DevOps: apply prompting to real operations tasks.
10AI Frameworks & RAG
- LangChain: building blocks for LLM-powered applications.
- LlamaIndex & Other Frameworks: data-connected AI tooling.
- RAG Fundamentals & Pipeline Components: retrieval-augmented generation end to end.
- DevOps RAG Use Cases: apply RAG to runbooks, docs and incident response.
11Vector Databases & MCP
- Vector DB Fundamentals & Popular Databases: embeddings and similarity search.
- Hands-on Operations: load, query and manage vector data.
- MCP Fundamentals & Building Servers: the Model Context Protocol and its components.
- DevOps MCP Use Cases: connect AI to your DevOps tools safely.
12AI Agents & LLMOps
- Agent Fundamentals & Building Agents: design autonomous, tool-using agents.
- DevOps Agent Use Cases: agents for delivery, SRE, security and cost.
- LLM Deployment: serve and scale models reliably.
- MLOps for AI Pipelines: operationalise and monitor AI workloads.
What you walk away with, and where you go next
The program is the start. You finish with proof of skill, then keep growing through our community and freelancing network.
A recognised certificate
Earn a CareerByteCode certificate that shows you completed a rigorous, hands-on DevOps and AI program.
A 51-project portfolio
Walk away with 51 real, production-style projects you can demo in interviews and link from your resume.
Job-ready skills
Confident, practical command of Linux, cloud, CI/CD, Kubernetes, Terraform and AI for DevOps.
Grow through our tech community
Join a global community of professionals. Showcase your projects, stay visible to the industry, and speed up the opportunities that come your way.
Earn through freelancing
Ready to turn your skills into income? Strong candidates are invited into our freelancing programs, where you learn to win clients and get paid for real work.
Ready to start the curriculum?
Join the next monthly batch and work through all twelve modules with live mentorship.