Updated July 2026

MLOps Engineer Roadmap

From experiment tracking and CI/CD to containerization, model serving, registries, monitoring, and Kubernetes — the structured, free path to becoming an MLOps engineer.

8 learning stages
10+ free courses & tutorials
6–9 mo part-time pace
01 —

MLOps Foundations & Reproducible Projects

3–4 weeks

Why 85% of ML models never reach production — and the practices that close that gap.

What you'll learn

  • The ML lifecycle: from notebook to monitored production service
  • Structuring ML repos for reproducibility
  • Git workflows for ML code
  • Data versioning with DVC

Courses

MLOps Foundations — Free · Start here
02 —

Experiment Tracking & Reproducibility

2–3 weeks

You can't operate what you can't reproduce. This is the discipline that makes every later stage possible.

What you'll learn

  • Logging parameters, metrics, and artifacts with MLflow
  • Comparing runs and reproducing past results
  • Tracking data and code lineage alongside model versions
  • Structuring experiments for team-wide reuse
03 —

CI/CD for Machine Learning

3–4 weeks

Treat model training and deployment like software — automated, tested, and triggered on every change.

What you'll learn

  • Automating training, testing, and deployment with GitHub Actions
  • Testing strategies for ML code and data, not just software logic
  • Automated retraining triggers and pipeline orchestration
  • Gating deployments on model quality checks, not just code review
04 —

Containerization & Model Serving

4–5 weeks

Package a model once, run it anywhere — and serve predictions fast enough to matter.

What you'll learn

  • Packaging models and dependencies with Docker
  • Building REST APIs for real-time inference with FastAPI
  • Batch vs. real-time serving trade-offs
  • Latency and throughput optimization for serving
05 —

Model Registry, Versioning & Feature Stores

3–4 weeks

Know exactly which model is live, what it was trained on, and how to roll it back in seconds.

What you'll learn

  • Model registries, promotion workflows, and rollback strategies
  • Feature stores and when they're actually worth the complexity
  • Keeping training and serving features consistent (avoiding skew)
  • Governance: who can promote a model, and how it's audited
06 —

Monitoring, Drift & Observability

3–4 weeks

A model that works on day one and silently decays by day ninety is the single most common production failure.

What you'll learn

  • Detecting data drift and concept drift without real-time labels
  • Alerting on performance degradation before it hits the business
  • Logging and observability for ML services, not just system metrics
  • Building a monitoring dashboard stakeholders actually check
07 —

Scaling on Kubernetes & Production Infrastructure

3–4 weeks

The infrastructure layer that turns a working service into one that survives real traffic.

What you'll learn

  • Deploying and scaling ML services on Kubernetes
  • Autoscaling and resource management for inference workloads
  • Canary and blue-green deployments for safe model rollouts
  • Cost-aware infrastructure choices (CPU vs. GPU, batch vs. always-on)
08 —

Capstone & Interview Preparation

Ongoing

Prove the full pipeline end to end, then prepare for the systems and operational-judgment questions the interview loop will ask.

What you'll learn

  • Build a complete pipeline: training → registry → serving → monitoring
  • Debug a production incident under simulated pressure
  • Design an MLOps platform from scratch on a whiteboard
  • Answer the operational trade-off questions that separate MLOps engineers from ML engineers

Ready to start?

All courses are free and backed by real open source products you can use immediately.

Start: MLOps Foundations Interview Prep → All Roadmaps →
AI Engineer Path → ML Engineer Path → Data Scientist Path → AI Research Scientist Path → AI Product Manager Path → Forward Deploy Engineer Path →