Basic MLOps Engineer Roadmap

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. Resources:

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. Resource

FastAPI Wrapping your model as a REST API. Resource: FastAPI's official tutorial clearest documentation I've ever seen + ArjanCodes on YouTube.

Testing with pytest

Writing unit and integration tests for ML code. Resource: pytest official docs + the "Python Testing with pytest" book.

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 post, take this

🎯 MLOps Zoomcamp by DataTalks.Club — completely free course on GitHub covering experiment tracking, orchestration, deployment, and monitoring with hands-on projects. Has an active Slack community. Search "MLOps Zoomcamp" on GitHub.

Within Phase 2, you'll cover

Data Versioning

DVC The Git for large datasets and models. Resource: DVC's official YouTube channel + dvc.org interactive tutorials.

Experiment Tracking

MLflow Track every experiment, compare runs, manage model versions. Resource: MLflow official docs + freeCodeCamp's "MLOps Course" on YouTube (6 hours).

Orchestration — Apache Airflow

Schedule and orchestrate your data and ML pipelines. Resource: Marc Lamberti on YouTube — the Airflow guru. His free crash course (4 hours) covers everything you need + Astronomer's Airflow Academy (free with certification).

Model Serving

FastAPI + BentoML Build production-ready inference APIs. Resource: BentoML documentation + their YouTube channel.

CI/CD — GitHub Actions

Automate your testing, building, and deployment pipelines. Resource: GitHub's official Actions tutorials + TechWorld with Nana's CI/CD crash course.

Bonus open-source gem: "Made With ML" by Goku Mohandas on GitHub. 35K+ stars. 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 MENA region (especially KSA & UAE)

GCP — Best ML services (Vertex AI, BigQuery ML)

Google Cloud Skills Boost

Azure — Common in enterprise companies

Microsoft Learn

Free resources: • freeCodeCamp's "AWS Certified Cloud Practitioner" on YouTube (14 hours) • Google Cloud Skills Boost — free labs after sign-up • AWS Skill Builder — free tier with foundational courses

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

Standard for infrastructure monitoring. TechWorld with Nana has great tutorials here too.

MLOps & ML Engineering

DevOps Tools

ML Intuition

Free Newsletters & Blogs

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 = a strong CV point.
Join communities. MLOps Community Slack, Hugging Face Discord, and of course MLOps MENA Community that 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.