Docker containerization, GPU orchestration (NVIDIA Container Toolkit), vLLM deployment, CI/CD pipelines, and cloud edge infrastructure.
devops
▶ 3:30:00
The definitive guide to containerizing local AI and LLM inference stacks. Learn how to configure the NVIDIA Container Toolkit, orchestrate vLLM with PagedAttention, deploy Ollama in Docker, and manage high-throughput persistent model volumes.
devops
▶ 1:00:00
A complete DevOps guide to building minimal, secure, and lightning-fast Docker container images with multi-stage builds, non-root users, and production Docker Compose configurations.
devops
▶ 4:20:00
Master SQL from beginner queries to advanced window functions. Learn SELECT, WHERE, INNER/LEFT JOINs, GROUP BY, indexes, and database schema design.
Run real-time scripts in our in-browser sandbox supporting Python, JavaScript, and relational SQL.
DevOps is the set of practices and tools that closes the gap between writing software and running it reliably in production. Instead of throwing finished code over a wall to a separate operations team, DevOps automates building, testing, deploying, and monitoring applications so that releases become routine instead of risky events. The tutorials in this category cover the working toolkit: Linux, Git, Docker containers, CI/CD pipelines, Kubernetes, cloud platforms, and infrastructure as code with tools like Terraform — the same combination you will meet in real engineering teams.
Build your foundations first: get comfortable on the Linux command line, learn Git properly, and write simple shell scripts to automate repetitive tasks. Next, learn Docker — packaging an application into a container is the single most useful DevOps skill — followed by a CI/CD tool such as GitHub Actions to automate tests and deployments on every change. Then add one cloud platform, infrastructure as code with Terraform, and Kubernetes for orchestrating containers at scale. Monitoring and logging come last in sequence but matter from day one: you cannot operate what you cannot observe. Work through everything in a home lab or a free-tier cloud account so every tutorial ends with something actually deployed and observable, not just notes for later reference.
DevOps is usually a second step rather than a first one: it builds on basic programming, Linux, and networking knowledge. Beginners can absolutely aim for it, but plan to spend time on those foundations first — junior roles typically expect you to already read code, write small scripts, and administer a Linux server comfortably before you touch production pipelines.
You need scripting ability rather than full application-development depth. Most DevOps work involves writing and reading Bash and Python scripts, YAML configuration, and automation code. Strong programmers have an advantage, but the role centers on systems, automation, and reliability far more than on building product features for end users.
Docker first, without question. Kubernetes orchestrates containers, so its core concepts — pods, images, networking between services — only make sense once you can build and run a single container yourself. A few weeks of small Docker projects will make Kubernetes dramatically easier to understand when you get there.