Learning paths Developers, career changers

Become an ML engineer

Deploy, monitor and maintain models in production

~14 weeks at 8 hours a week · 3 courses

Plan it around your week

This path is roughly 112 hours in total. Tell us how much time you actually have.

About 14 weeks.

Based on our own estimate of the path, not the providers' figures. Most people need a bit longer than they think.

How long does it take?

Courses3
WeeksAbout 14
Hours a week8
Total hoursAbout 112

The steps, in order

  1. 1
    Difficulty: 2 of 5

    Why this step: Confirms the fundamentals are solid before you go further.

    You're ready for the next step when: You're confident training and validating basic models.

  2. 2
    Machine Learning Specialization

    DeepLearning.AI and Stanford

    Difficulty: 3 of 5

    Why this step: The clearest foundations course for classic machine learning; check for a no-certificate option when you enrol.

    You're ready for the next step when: You can build and tune linear models, trees, neural networks and clustering models.

  3. 3
    Difficulty: 4 of 5

    Why this step: Adds the deployment and productionisation mindset fast.ai is known for.

    You're ready for the next step when: You can train, evaluate and ship a deep learning model end to end.

Where to go next

Want the bigger picture?

Roadmaps show every skill in a field, in order, so you can see where this path fits.