This is the roadmap I'd give my younger self — focused on free, open-source resources because the real knowledge in this field lives on GitHub and YouTube, not behind expensive paywalls.
If you're a student, ML Engineer, Software Engineer, or Data Scientist looking to break into MLOps, this is for you. Realistic timeline: 6-9 months at 10-15 hours/week.
Phase 0 — Foundations (Month 1-2)
Before you touch any MLOps tool, build a solid foundation in four areas.
Python
OOP, decorators, virtual environments, type hints, numpy/pandas/scikit-learn.
Linux & Bash
Every production server runs Linux. You need to be comfortable on the command line.
Git & GitHub
Branching, merging, pull requests, working in a team.
ML fundamentals
You don't need a PhD, but you need to understand the basics: supervised vs unsupervised, train/val/test splits, overfitting, evaluation metrics.
- Andrew Ng's "Machine Learning Specialization" (Coursera, audit free)
- StatQuest with Josh Starmer (YouTube)
Phase 0 project
Build a small ML project with proper folder structure, virtual environment, and a clean GitHub repo with a good README.
Phase 1 — Software Engineering for ML (Month 3)
This is what separates an MLOps Engineer from a Jupyter notebook user.
Docker
Containerizing applications is the #1 MLOps skill.
FastAPI
Wrapping your model as a REST API. FastAPI's official tutorial is the clearest documentation I've ever seen — pair it with ArjanCodes on YouTube.
Testing with pytest
Writing unit and integration tests for ML code.
Phase 1 project
Take your Phase 0 model, wrap it in FastAPI, containerize it with Docker, write tests, and push to GitHub.
Phase 2 — MLOps Core (Month 4-5)
If you take only ONE thing from this entire roadmap, take this: 🎯 MLOps Zoomcamp by DataTalks.Club — a completely free course on GitHub covering experiment tracking, orchestration, deployment, and monitoring, with hands-on projects and an active Slack community.
Within Phase 2, you'll cover the following.
Data versioning — DVC
Git for large datasets and models. Start with DVC's official YouTube channel and the interactive tutorials on dvc.org.
Experiment tracking — MLflow
Track every experiment, compare runs, manage model versions.
Orchestration — Apache Airflow
Schedule and orchestrate your data and ML pipelines. Marc Lamberti is the Airflow guru — his free crash course (4 hours) covers everything you need, and Astronomer's Airflow Academy is free with certification.
- Marc Lamberti's YouTube (Data with Marc)
- Airflow official docs
- Astronomer Academy (free Airflow certification)
Model serving — FastAPI + BentoML
Build production-ready inference APIs.
CI/CD — GitHub Actions
Automate your testing, building, and deployment pipelines.
Bonus open-source gem
"Made With ML" by Goku Mohandas — 35K+ stars, a complete MLOps course with code, free.
Phase 3 — Cloud Basics (Month 6)
Pick ONE cloud provider in the beginning. Don't try to learn three at once.
AWS — most common in the MENA region (especially KSA & UAE)
GCP — best ML services (Vertex AI, BigQuery ML)
Azure — common in enterprise companies
Focus on object storage (S3/GCS), compute (EC2/Compute Engine), container services, and ML-specific services (SageMaker/Vertex AI).
Phase 4 — Basic Monitoring (Month 7)
Evidently AI
Open-source toolkit for data drift and model monitoring. Their YouTube channel has full workshops.
Prometheus + Grafana
The standard for infrastructure monitoring. TechWorld with Nana has great tutorials here too.
YouTube Channels You MUST Subscribe To
MLOps & ML engineering
DevOps tools
ML intuition
Free Newsletters & Blogs
- The Batch (Andrew Ng)
- MLOps Community newsletter
- Hugging Face blog
- Sebastian Raschka's "Ahead of AI"
My Honest Advice From Personal Experience
- Build projects, don't just watch videos. End every phase with a GitHub project. Your portfolio matters more than any certificate.
- Contribute to open source. One accepted PR to MLflow or Evidently is a strong CV point.
- Join communities. MLOps Community Slack, Hugging Face Discord, and of course the MLOps MENA Community we're building together.
- Write about what you learn. LinkedIn or Medium. Writing crystallizes knowledge and builds your personal brand.
- Don't rush the foundations. Tools change, concepts last.