DevOps and cloud-native training
DevOps training for cloud-native teams
Containers, orchestration, automated pipelines and one major cloud platform. Here is how the training is laid out, what each course covers, and how the pieces fit into a pipeline you build yourself.
Containers first
Why Docker shows up in nearly every DevOps roadmap
Docker containerizes an application so it runs the same way on your laptop and in production. That consistency is why it appears in almost every DevOps curriculum, from university professional courses to bootcamp syllabi. In our training you write a Dockerfile, build an image, and run a multi-container application locally before you touch any cloud environment. Skipping this step makes everything after it harder to debug — the pipeline failures you meet later almost always trace back to something that behaved differently outside the container.
You also learn what a container is not. It is not a virtual machine, and the differences in startup time, filesystem layers and isolation shape how you design the rest of a deployment. Working through image layers by hand once makes registry size and build caching obvious later.
What the container module covers
- Write and read a Dockerfile line by line
- Build, tag and push an image
- Compose a multi-container app locally
- Debug a container that starts and exits
Kubernetes and container orchestration
Kubernetes orchestrates containers across many machines: scheduling them, restarting failed ones, and managing networking between services. It is a central topic in DevOps training because it is what production environments run.
What you deploy in this module
Cluster architecture, deployments, services and configuration. You deploy a containerized application to a Kubernetes cluster running on a cloud platform — the same workflow university courses use to teach the material. You will read the cluster state, watch a rolling update replace pods, and trace why a service stopped routing to a pod that looked healthy.
CI/CD pipelines: automation from commit to deployment
A CI/CD pipeline automates the path from a code commit to a running deployment. You build one in stages rather than configuring a finished example, so each step has a reason you can explain.
Run tests on every push
Test job
Wired
Build the container image
Build job
Wired
Deploy to staging
Deploy job
Wired
Manual approval gate before production
Human step
Wired
Most real teams keep a person between staging and production, so the course keeps one too. By the end you have a pipeline you configured yourself and can walk through in an interview, including the choices you made about caching, parallel jobs and rollback.
A pipeline you can explain beats a list of tools you have heard of.
Cloud platform
Cloud platforms as part of the curriculum, not an afterthought
Cloud platforms appear inside DevOps coursework because that is where deployments happen. Our training uses a major cloud provider's environment for the Kubernetes deployment modules, so you work with real infrastructure and real console interfaces rather than a simulator.
You also cover the parts that transfer across providers: compute instances, managed Kubernetes services, and identity permissions. Those concepts carry over even if an employer runs a different platform, because the shape of the problem is the same — you are deciding what runs where, who may reach it, and how it gets replaced.
Want to know which platform the modules run on before you commit? Ask which cloud platform is used.
Transferable across providers
- Compute instances and how they are sized
- Managed Kubernetes services
- Identity and permission boundaries
DevSecOps: putting security checks inside the pipeline
DevSecOps adds security testing to the pipeline rather than running it as a separate phase at the end. In practice that means scanning container images for known issues, checking dependencies, and managing secrets so credentials never sit in a repository.
Where the two tracks meet
The DevSecOps modules build on the CI/CD course and assume you have completed it. This is the point where DevOps and cybersecurity training overlap: the same pipeline you automated now refuses to ship an image with a known issue in it, and the same repository that held your manifests now holds no credentials at all. If you have already worked through our cybersecurity courses, the scanning and secret-handling labs will look familiar from the other side.
Checks you add
- Container image scanning on every build
- Dependency review before merge
- Secret handling outside the repository
- Least-privilege service permissions
Choosing between self-paced and accelerated DevOps training
Self-paced works if your schedule varies or you are learning alongside a job. Accelerated cohorts suit people who can commit full days and want external deadlines. DevOps material has more moving parts than most subjects, and some learners find the cohort structure helps them push through the orchestration modules. Either way, the module content is the same.
Self-paced
Start when you can, keep your own hours
Suits learners who study around a job, a shift pattern or family commitments. There is no weekly deadline, and you can sit with the orchestration labs for as long as they take. The trade-off is that nothing external pushes you forward — the plan is yours to hold.
Open enrolment
Accelerated cohort
Fixed dates, full days, shared deadlines
Runs to a published schedule, so the pipeline and cluster labs land in sequence and you meet the same deadlines as everyone in the group. Better for learners who need a calendar to move, and for anyone who wants the orchestration modules behind them by a specific date.
Cohort dates
Portfolio project
Building a portfolio that shows your work
DevOps interviews often include a walkthrough of something you built. Keep your pipeline configuration and deployment manifests in a public repository, with a short README explaining what each stage does and why you made the choices you made. A working pipeline you can explain beats a list of tool names on a resume.
The final modules include a project structured for exactly this purpose: a repository you own, a pipeline that runs on every push, and a written account of the decisions behind it. If you are still deciding which direction to point the project at, the career pathways page maps course combinations to role families — platform engineering, site reliability work, cloud operations and DevSecOps — so you can aim the final build at the one you want.
Where DevOps skills lead
DevOps and cloud skills feed into several role directions. Each leans on the same foundation with a different emphasis, and you do not have to decide before you start the foundation modules.
Platform engineering
Pipelines and internal tooling
Core path
Site reliability work
Clusters, uptime, incident response
Core path
Cloud operations
Compute, permissions, cost awareness
Core path
DevSecOps
Security checks inside delivery
Needs the CI/CD course
The DevSecOps direction is marked differently because it has a real prerequisite: the CI/CD course comes first, then the security modules build on it. Everything else starts from the same foundation.
Questions learners ask before starting
Do I need to know Linux before starting DevOps training?
Basic command-line comfort helps. Our foundation module covers the Linux skills you will use in the container and pipeline labs, so you can start without prior experience if you complete it carefully. The labs assume you can move around a filesystem, read a process list and edit a file from a terminal — if any of that is new, the foundation module is where you build it.
Which cloud platform does the training use?
The Kubernetes deployment modules run on Google Cloud, the same environment used in university DevOps courses covering containerization. The underlying concepts — compute, orchestration and permissions — transfer to other major platforms, so what you practise is the pattern rather than one vendor's menu.
How much time should I budget per week?
Most learners spend four to six hours per week on a self-paced DevOps course. The pipeline and orchestration modules take longer than the introductory ones. If you can only manage two hours weekly, expect the full path to take proportionally longer — the modules are sequential and the labs do not shrink.
Can I take the Kubernetes module before the container module?
The container module is a prerequisite in practice, even where it is not enforced by the platform. Orchestration labs assume you can build and run an image and read what a container is doing when it misbehaves. Learners who skip ahead tend to spend the time they saved on debugging environment problems instead of learning the cluster.
Next step: get the syllabus and read the module list
The syllabus lays out every module in order, with the lab each one ends in and the prerequisites between them. If you would rather compare this path against our cybersecurity or AI tracks before deciding, the course comparison page puts the formats and module counts side by side. And if you have a question the syllabus does not answer, write to us — we answer Monday to Friday, 9:00 AM to 6:00 PM Central Time.
Talk to the team
Robimhood Digital
88 Innovation Drive
Austin, TX 78701, USA
Monday–Friday, 9:00 AM – 6:00 PM Central Time