AutomateEdge · Roadmap

MLOps Engineer Roadmap: Complete 2026 Study Plan

Build and own the infrastructure that keeps ML models reliable in production — from CI/CD pipelines and model registries to drift detection, GPU orchestration, and multi-model serving at scale.

MLOps Engineers exist because ML systems break in ways software systems do not. Data drifts. Training-serving skew accumulates silently. GPU clusters burn money while idle. Models pass tests at deploy time and degrade six weeks later with no alarm. This track teaches you to build the platform layer that makes ML teams fast, safe, and measurable. You will own Kubernetes-based serving infrastructure, CI/CD pipelines tuned for ML artefacts, feature stores with online-offline parity, drift detection systems, and the observability stack that catches silent regressions before they reach users. The role sits at the intersection of platform engineering and ML — and it pays like it.
24 weeks 10h / week Free AI-curated

Explore the MLOps Engineer track

This track is organised as six deep-dive pages. Start with Skills if you are planning your learning; start with Salary if you are weighing a career switch.

Skills matrix →

The concrete skill ladder with 2026-calibrated proficiency targets.

Tools inventory →

Every tool you install in your first year, with hours-to-learn estimates.

Portfolio projects →

10-15 shippable build ideas with GitHub evaluation rubrics.

Certifications →

Free certs worth completing — ranked by employer-signal strength.

Salary bands →

US + regional comp data, 2026 Q1, refreshed quarterly.

Career path →

Entry to principal progression, with pay and promotion markers.

Ready to start?

AutomateEdge gives every learner a free, personalized study plan across the track. The platform tracks your weekly progress, evaluates your GitHub projects against the rubrics shown here, and issues a verifiable certificate when you complete the track.

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