Built by engineers running production Kubernetes and GPU infrastructure at scale, and read on the Kubenatives newsletter.
Most Kubernetes content teaches you the wrong things.
Certifications teach you command syntax. YouTube tutorials show happy paths. Blog posts explain features in isolation.
None of them teach you what actually matters in production:
- •Why your vLLM pods OOM at 3 AM and how to tell which type of OOM it is
- •What nvidia-smi output actually means (most engineers read it wrong)
- •When MIG partitioning saves you $50K/month and when it doesn't
- •Why your distributed training is 3x slower on Kubernetes than bare metal
- •How to upgrade Kubernetes without taking down production
DevOpsBeast courses teach the reasoning frameworks and operational knowledge you need when the outage is happening at 3 AM and the documentation doesn't help.
Start free.
Before you buy anything, take one of our free courses. No email required. No credit card. Read it, share it, use it.
Networking Fundamentals for Engineers
The TCP/IP, DNS, and network troubleshooting knowledge every engineer is expected to know, but most never formally learn.
- How a packet travels from your laptop to a Kubernetes pod
- The tcpdump commands that solve 80% of network issues
- What CNI plugins actually do (and what they don't)
Linux Fundamentals for Engineers
For engineers who use Linux every day but never formally learned it. Filesystems, processes, systemd, cgroups, and namespaces.
- How the kernel, userspace, and syscalls fit together
- systemd, journalctl, and reading /proc to debug anything
- cgroups and namespaces: the building blocks of every container
Docker & Container Fundamentals
For engineers who use Docker every day but never understood what it actually is. No more magic, no more guessing.
- What Docker really is: dockerd, containerd, runc, and the OCI spec
- Image layers, caching, and Dockerfiles that cut size 10×
- Production debugging: won't start, slow, or broken networking
Git Internals for Engineers
For engineers who use Git every day but never understood what it actually does. A content-addressable filesystem you already know how to use.
- Blobs, trees, commits, refs: Git is just a filesystem of hashes
- Rebase, reset, and reflog: rewriting history without losing work
- Cherry-pick, bisect, blame, and pickaxe for real debugging
Bash & Shell Scripting for Engineers
The actual minimum for writing shell scripts that do not break in production. Parsing, quoting, error handling, ShellCheck.
- How Bash parses a script: word splitting, quoting, expansion order
- `set -euo pipefail` and the traps pattern for production scripts
- ShellCheck in CI, structured debugging, and when to switch to Python
Observability Fundamentals for Engineers
Metrics, logs, traces, and SLOs: the third pillar of engineering that most engineers learn wrong.
- The four golden signals, cardinality budgets, and Prometheus done right
- Structured logging, OpenTelemetry tracing, and sampling that keeps costs sane
- SLIs, SLOs, error budgets, and alerts that signal instead of spamming
When you're ready to go deeper.
Production-grade courses for Kubernetes, GPUs, and LLM infrastructure. Lifetime access, no subscription.
Production GPU Infrastructure on Kubernetes
Running GPU workloads on Kubernetes in production, from drivers to serving.
LLM Operations for MLOps Engineers
31 LLM concepts through the lens of operating them at scale.
Agentic AI Platform Engineering
The platform layer beneath the agents, for engineers who operate them rather than write them.
Kubernetes Performance Optimization
For the interview question and the incident: your cluster is slow, what do you do?
Kubernetes Security for DevOps Engineers
Secure clusters the way attackers think about them.
Identity and Trust for DevOps Engineers
From TLS handshakes to zero trust, for engineers who need identity to finally make sense.
SSL/TLS & Certificate Management for Kubernetes Engineers
From encryption fundamentals to the 3 AM cert expiry runbook.
Kubernetes System Design Interview Prep
The structured framework for senior and staff Kubernetes design interviews.
Distributed Systems Design
Consensus, consistency, and the hard problems that only appear at scale.
Architecture and Scale
The patterns and data structures that make systems hold up at scale.
Kubernetes Cluster Upgrades with kubeadm
Upgrading production clusters with kubeadm, from planning to worker rollout.
Production LLM Inference on Kubernetes
Running LLM inference on self-managed Kubernetes, from real H100 deployments.
Production RAG Infrastructure on Kubernetes
For engineers running retrieval in production who need it to work at millions of documents.
Terraform in Production
For engineers who apply to production and cannot afford to be surprised.
CI/CD Pipeline Engineering
For engineers who own the pipeline everyone else depends on.
Running Databases and Stateful Systems on Kubernetes
For engineers who have to run the database, not just the cluster it sits on.
etcd Operations Masterclass
The storage engine behind every Kubernetes cluster, and how to keep it running.
Production Kubernetes Operations
The Day 2 playbook. The knowledge teams usually learn the hard way.
Kubernetes Architecture & Chaos
How Kubernetes works under the hood, and how to test that it holds.
GPU Cost Optimization on Kubernetes
Cut GPU spend in half without breaking production.
Kubernetes Networking
Find the broken hop rather than guess at it. CNI agnostic.
Kubernetes Debugging for SREs
The systematic debugging playbook for the 3 AM incident.
Container Internals and Runtime Engineering
What happens beneath the container abstraction, at the kernel and runtime level.
Networking and API Design for System Design
The communication layer of system design, from packets to APIs.
GitOps with Argo CD
Running Argo CD as critical infrastructure, for the team that owns deployment.
Software Supply Chain Security
Proving what an artifact is, and deciding whether to trust it.
Production Systems Engineering
The production half of system design: deploy it, observe it, secure it.
eBPF & Cilium for Platform Engineers
Understand eBPF deeply enough to reason about it, and Cilium well enough to run it.
Designing Large-Scale GPU Clusters on Kubernetes
Designing the Kubernetes platform underneath training and inference on the same GPUs.
Buy the track, or take the whole library
Courses start at $79 on their own. Bundle up for lifetime access to more, for less. No subscription.
GPU & LLM Production Bundle
The full GPU stack for AI platform engineers: training at fleet scale, inference in production, and the cost of both.
6 courses, one price
System Design Series
All four System Design courses: the communication layer, distributed systems, architecture and scale, and production.
4 courses, one price
Kubernetes Operations Bundle
Running clusters well, debugging them under pressure, upgrading them safely, running the databases on top of them, and repairing all of it when none of that worked.
7 courses, one price
Kubernetes Security & Identity Bundle
Cluster security, identity and certificates, supply chain integrity, and container runtime internals.
5 courses, one price
Build & Delivery Bundle
The path from a commit to production: the pipeline that validates it, the provenance that makes it trustworthy, and Argo CD delivering what it produced.
3 courses, one price
All-Access Pass
Every paid course, forever. Including every future release.
All 31 paid courses
Why text, not video?
Search and reference
When production is on fire at 3 AM, you can search a text document in seconds. You can't search a 30-minute video.
Respects your time
Read a lesson in 10 minutes. Watch the same content in 30.
Code you can copy
Every command and YAML snippet is copy-paste ready. No pausing videos to retype.
Updated frequently
Text is easy to maintain. Kubernetes moves fast, and our content moves with it.
Who this is for.
DevOpsBeast courses are built for engineers who already have production experience and want to go deeper. If you're just starting with Kubernetes, our free Networking Fundamentals course is the right place to begin.
If you run production Kubernetes, deploy ML models on GPUs, interview for senior DevOps or platform roles, or upgrade clusters without breaking them, these courses are for you.
What DevOps Engineers Are Saying
Going through the course helped me connect many of the dots around the errors and challenges I faced while setting up GPU clusters and managing workloads in my current role. I highly recommend DevOpsBeast to anyone looking for deep practical experience and not just theory.
30-day money-back guarantee.
Try any course for 30 days. If it's not what you expected, email us and we'll refund you. No forms, no questions.
About
DevOpsBeast is built by Sharon Sahadevan, a production K8s and ML infrastructure engineer with hands-on experience running GPU workloads, upgrading clusters, and solving the kind of problems that don't have Stack Overflow answers.
Sharon also writes the Kubenatives newsletter, read by 3,500+ engineers learning production Kubernetes and DevOps weekly.