# blog
The video lessons, in writing — roadmaps, tutorials, and lessons learned in production.
# featured
Linux for DevOps · Part 6/19Part 6 of the free Linux for DevOps course: why you should never log in as root, why every user is really a number, how to create a user with useradd, set its password with passwd, add it to groups with usermod -aG, read the user database in /etc/passwd and /etc/group, and run a command as anyone with sudo -u.
Muhammad Rashid12 min read
Muhammad Rashid13 min readPart 5 of the free Linux for DevOps course: count lines with wc, group and count duplicates with sort and uniq, cut a slice out of a huge file with sed, pull columns and sums out of text with awk, and chain them all together with the pipe to turn 200,000 log lines into one clean answer.
Muhammad Rashid14 min readPart 4 of the free Linux for DevOps course: create a file with touch, edit it in nano and actually get back out, print it with cat, read just the top or bottom with head and tail, add lines with >>, and find the one line that matters with grep.
Muhammad Rashid13 min readPart 3 of the free Linux for DevOps course: the map of a Linux system — what /etc, /var and /home are really for — plus pwd, ls -la, cd, mkdir -p, touch, cp, mv, rm, find and head/tail, with the mistakes that catch every beginner.
Muhammad Rashid9 min readPart 2 of the free Linux for DevOps course: what Linux actually is, the difference between a kernel and a distro, why cloud VMs, containers, CI runners and Kubernetes nodes are all Linux, how you reach a server with SSH, and the three commands to run on any machine you've never seen.
Muhammad Rashid10 min readPart 1 of the free Linux for DevOps course: why more than 9 out of 10 cloud servers run Linux, what kernel, distro and shell really mean, how to install Ubuntu on Windows (WSL) or Mac (Multipass) in five minutes, and your first commands — plus the full 18-lesson roadmap.
Muhammad Rashid11 min readThe finale: assemble the whole series — FastAPI, Docker, GitHub Actions, ECR, Terraform, EKS, MLflow, Prometheus/Grafana, and drift-triggered retraining — into one platform, and trace a change from git push to a promoted model serving traffic. Part 11 of the MLOps series.
Muhammad Rashid10 min readA model can fail with no code change at all: the world moves on and your frozen model goes stale. Detect data and concept drift with a KS test, and build a retraining pipeline that only ships a new model if it beats the current one. Part 10 of the MLOps series.
Muhammad Rashid10 min readA running model you can't see is one you can't trust. Add a real /metrics endpoint, scrape it with Prometheus, chart it in Grafana, and alert on latency, errors, and the shape of your predictions. Part 9 of the MLOps series.
Muhammad Rashid11 min readStop building infrastructure by hand. Provision the VPC, EKS cluster, ECR, S3, and the GitHub OIDC role with Terraform — reproducible, reviewable, and easy to destroy on demand. Part 8 of the MLOps series.
Muhammad Rashid11 min readTake the inference container to a real cluster: Deployments, Services, Ingress, ConfigMaps, Secrets, and an HPA — with the readiness probe we've been building since part two finally doing its job. Part 7 of the MLOps series.
Muhammad Rashid9 min readGit tracks your code and MLflow tracks your models, but which dataset trained which model? DVC versions data the way Git versions code: a tiny pointer in Git, the bytes in S3. Hands-on init, add, remote, push, pull. Part 6 of the MLOps series.
Muhammad Rashid11 min readStop overwriting model.joblib. Track every experiment's params and metrics, register versions, and promote by a champion/challenger gate with MLflow, so 'which model is in production, and is it actually better' has an answer. Part 5 of the MLOps series.
Muhammad Rashid10 min readAutomate the gate: test the model API, build the image, scan it for CVEs with Trivy, and push to Amazon ECR — with OIDC instead of long-lived keys and the git SHA as an immutable image tag. Part 4 of the MLOps series.
Muhammad Rashid10 min readPackage the model, the exact scikit-learn version, and the API into one image that runs the same everywhere. Multi-stage builds, a lean non-root image, .dockerignore, and Docker Compose. Part 3 of the MLOps series.
Muhammad Rashid10 min readTurn a model artifact into a real prediction API — request validation with Pydantic, automatic Swagger docs, real-time vs batch inference, and a Docker-ready project structure. Part 2 of the MLOps series.
Muhammad Rashid9 min readMLOps explained for DevOps engineers — what it is, why plain DevOps isn't enough for ML, and a hands-on look at training vs inference using a real scikit-learn model. Part 1 of an 11-part series.
Muhammad Rashid7 min readDesign and deploy a production-grade 3-tier web app on AWS with Terraform, ECS Fargate, RDS Multi-AZ, and a GitHub Actions CI/CD pipeline. The complete build, the reasoning, and the repo you can run yourself.
Muhammad Rashid8 min readThe exact order to learn DevOps in 2026 — Linux, Git, Docker, CI/CD, cloud, Terraform, Kubernetes, and monitoring — and the mistakes that keep beginners stuck.
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