MLOps Engineer Roadmap
You build the platforms that keep machine learning alive in production - DevOps discipline applied to models, data and GPUs. Indicative 2026 salaries $100-135k US; almost always a second role reached from DevOps/platform or ML engineering. Scarce skills, small talent pool.
By Carl Mills • Last updated • tap any step for guidance and free resources • progress saves in your browser • free printable PDF roadmap poster.
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MLOps Engineer roadmap FAQ
MLOps vs ML engineer - are they the same job?
They overlap heavily at small companies. Where they split: ML engineers own models (training, quality), MLOps engineers own the platform (pipelines, serving infrastructure, monitoring, cost) that many models run on. MLOps is the more ops-flavoured seat.
Which route in - DevOps or data science?
DevOps is the stronger base: the hard parts of MLOps are infrastructure, CI/CD and reliability, and the ML specifics can be learned on top. Follow the DevOps/Cloud roadmap first if you are starting fresh.
What tools should I learn?
Foundations beat tools: Docker, Kubernetes basics, Terraform, CI/CD, plus ML-specific layers - experiment tracking (MLflow/W&B), an orchestrator, a model registry, and serving/monitoring patterns. Named platforms change; the lifecycle does not.
What next once the list is green?
Prove it under pressure: take the free Mock Interview, then check your application signals.
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