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LEARNING PATH · INTERMEDIATE

Data Scientist → ML Engineer

Move from model building to production ML systems

Transition from data science (analysis and modeling) to ML engineering (production systems). Master MLOps, containerization, and model serving to build scalable machine learning systems.

6–12 months
8 hrs/week
2 tracks
$150,000–$210,000
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TARGET ROLE

Machine Learning Engineer, MLOps Engineer

SALARY RANGE

$150,000–$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

Specialize in production ML systems

02

AI Job Seeker

Land ML engineering roles

LEARNING OUTCOMES

What You'll Be Able To Do

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

Master MLOps fundamentals and model lifecycle management

Deploy models using Docker, Kubernetes, and cloud platforms

Build REST APIs for model serving with FastAPI or similar

Implement CI/CD pipelines for machine learning workflows

Monitor model performance and handle drift detection

Build a production ML project for your portfolio

FAQ

Common Questions

Do I need to relearn statistics and ML theory?+
No. As a data scientist, you already know ML fundamentals. This path focuses on engineering practices: code quality, testing, deployment, and scalability.
What's the biggest skill gap I need to fill?+
Software engineering practices. Data scientists often write exploratory code; ML engineers write maintainable, tested, deployed code. Focus on version control, testing, and DevOps.
Can I do this while working?+
Yes. Most learners complete this 6-month path at 8 hours/week while employed. Building a real project is the best way to learn.

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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