Machine Learning Engineer Roadmap

A step-by-step Machine Learning Engineer roadmap: the full stack, the skills to learn in order and the best places to learn each step.

The stack, end to end

  • Backend: Python, Lists, dictionaries and sets, Functions and modules, Virtual environments
  • Data: pandas, NumPy, Data cleaning, Jupyter notebooks, Data visualisation
  • AI and ML: Machine learning, scikit-learn, Model evaluation, Feature engineering, Neural networks, PyTorch, TensorFlow, Computer vision, Natural language processing, Model deployment, Experiment tracking, Monitoring models, Rules of ML, Prompt engineering, LLM APIs, Tool use, Retrieval (RAG), Evaluation
  • DevOps: Docker, Dockerfile, Docker Compose, Container networking
  • Get hired: Portfolio projects, Technical resume, Interview preparation

  1. Python

    Suggested pace: Start here

    Python is readable, popular and used for web, automation, data and AI. Learn the syntax, data structures, functions, files, errors, virtual environments and packages.

    Skills

    • Python
    • Lists, dictionaries and sets
    • Functions and modules
    • Virtual environments

    Tips

    • Automate something boring in your own life. It keeps motivation high.

    Where to learn

  2. Python for data: pandas and NumPy

    Suggested pace: Next

    Load, clean, reshape and summarise data with pandas and NumPy, and draw charts with Matplotlib or similar. Notebooks let you show your working.

    Skills

    • pandas
    • NumPy
    • Data cleaning
    • Jupyter notebooks
    • Data visualisation

    Tips

    • Pick a public dataset you care about and publish a short write-up of what you found.

    Where to learn

  3. Machine learning fundamentals

    Suggested pace: Then

    Learn how models learn from data: supervised and unsupervised learning, training and test sets, overfitting, metrics, and how to use scikit-learn.

    Skills

    • Machine learning
    • scikit-learn
    • Model evaluation
    • Feature engineering

    Tips

    • Spend more time understanding the data than choosing the model.

    Where to learn

  4. Deep learning

    Suggested pace: After that

    Neural networks power image, speech and language models. Learn the ideas, then use PyTorch or TensorFlow on a small project.

    Skills

    • Neural networks
    • PyTorch
    • TensorFlow
    • Computer vision
    • Natural language processing

    Tips

    • Re-use pre-trained models before training your own.

    Where to learn

  5. Putting models into production

    Suggested pace: Later

    A model is only useful when it runs reliably. Learn how to package, serve, monitor and update models, and how to track experiments and data versions.

    Skills

    • Model deployment
    • Experiment tracking
    • Monitoring models
    • Rules of ML

    Tips

    • Start with a simple model in production before a complicated one in a notebook.

    Where to learn

  6. Building with large language models

    Suggested pace: Later

    Learn to build features with LLMs: prompting, structured output, tool use, retrieval from your own documents, evaluation and cost and safety basics.

    Skills

    • Prompt engineering
    • LLM APIs
    • Tool use
    • Retrieval (RAG)
    • Evaluation

    Tips

    • Test with real examples and keep a list of cases that must keep working as you change prompts.

    Where to learn

    Free AI tutorials on Learn

  7. Docker and containers

    Suggested pace: Later

    A container packages an app with everything it needs so it runs the same everywhere. Learn images, containers, volumes, networks, Dockerfiles and Docker Compose.

    Skills

    • Docker
    • Dockerfile
    • Docker Compose
    • Container networking

    Tips

    • Containerise one of your own projects end to end.

    Where to learn

  8. Build your portfolio, resume and interview skills

    Suggested pace: When you are ready

    Employers want proof. Finish two or three projects you can show and explain, put them on GitHub, write a clear resume that matches the jobs you apply for, and practise the interview questions for the role.

    Skills

    • Portfolio projects
    • Technical resume
    • Interview preparation

    Tips

    • Describe what you built, why, and what you would improve. That is what interviewers ask about.

    Build your resume · Check your resume (ATS) · Interview questions · Salaries · Browse jobs

Frequently asked questions

Where should I start with Machine Learning Engineer?

Start at step 1 and work down in order, because later steps build on earlier ones. If you are signed in, the roadmap compares your resume with each step and marks where you could start.

Do I need a degree to work as a Machine Learning Engineer?

It depends on the employer and the country. Many employers accept proven skills and projects, while others ask for a degree. Read the adverts for the roles you want and ask the employer if it is unclear.

How do I know I am ready to apply?

When you can build a small project from start to finish without copying a tutorial, and explain why you made your choices. Apply while you keep learning; you do not need to know everything.