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LEARNING PATH · INTERMEDIATE

DevOps Engineer → MLOps Engineer

Bring DevOps rigor to machine learning lifecycles

DevOps engineers have the infrastructure and automation skills needed for MLOps. This path bridges the gap by teaching ML concepts and the unique challenges of deploying, monitoring, and versioning models at scale.

4–8 months
8 hrs/week
2 tracks
$145,000–$210,000
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TARGET ROLE

MLOps Engineer, ML Infrastructure Engineer

SALARY RANGE

$145,000–$210,000

DIFFICULTY

Intermediate

WHAT'S INCLUDED

Tracks in This Path

This path combines 2 curated learning tracks, sequenced to build on each other.

01

AI Professional

Master ML infrastructure and lifecycle

02

AI Job Seeker

Land MLOps engineering roles

LEARNING OUTCOMES

What You'll Be Able To Do

By the end of this path, you'll have concrete, job-ready skills.

Understand core ML concepts and the model lifecycle

Implement model versioning and experiment tracking with MLflow

Deploy models using Kubernetes and containerization

Build feature stores and model registries

Set up monitoring for model drift and performance degradation

Design an end-to-end ML deployment pipeline

FAQ

Common Questions

How much ML knowledge do I actually need?+
Less than you think. Understand what models do, how they degrade, and how to monitor them. You don't need to train or tune models.
Do my Kubernetes and CI/CD skills transfer directly?+
Yes, absolutely. MLOps uses the same tools but adds model-specific concerns: versioning, serving, monitoring, and retraining.
Is MLOps harder than DevOps?+
Not harder—different. You'll apply your DevOps strengths (automation, monitoring, reliability) to ML-specific challenges.

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Ready to start this path?

Take our 2-minute quiz to confirm this is the right path for you — or dive straight in.

Take the Quiz FirstStart Track 1

Last updated: 2026-03-07