ML Engineer Roadmap
Build, deploy and operate machine-learning systems.
Who this is for: software developers and data scientists who want to put models into production and keep them working. This is the longest roadmap because it combines two jobs. You should already be comfortable writing Python and using Git; if not, finish the Python Developer roadmap first. Plan for 12–15 hours a week over eight to twelve months.
ML engineer vs data scientist. A data scientist answers "can a model solve this?" An ML engineer answers "can this model run every day, on new data, without anyone babysitting it?" That means tests, containers, APIs, scheduled retraining and monitoring for when the model quietly gets worse. The first stage here is pure software engineering for that reason.
How to use it: carry one project through every stage. Train a credit-risk model in stage 2, wrap it in the API you learnt to build in stage 1, add a deep-learning variant in stage 3, automate its retraining in stage 4 and deploy it with monitoring in stage 5. One system built end to end teaches more than five notebooks.
Cost note: you do not need paid GPUs for most of this. Google Colab's free tier covers the deep learning stage, and the cloud stage can be done on free-tier credits if you shut resources down when you finish.
Common mistakes: leaving model files out of version control, no record of which data trained which model, and no alert when input data changes shape.
Software engineering
6 weeksWrite Python like a backend engineer: tested modules, clean Git history, Docker images that run the same everywhere, and HTTP APIs with input validation. Models are useless if the code around them is fragile.
- Advanced Python
- Testing
- Git
- Docker
- APIs
Milestone project: Dockerised REST API
Learn APIs on BeeProton →Machine learning
8 weeksTrain strong tabular models with scikit-learn and gradient boosting, evaluate them with the right metrics and cross-validation, and keep feature code reusable so training and serving compute features identically.
- scikit-learn
- XGBoost
- Feature stores
- Evaluation
Milestone project: Credit risk model
Learn Machine Learning on BeeProton →Deep learning
8 weeksUse PyTorch to train and fine-tune networks for images and text. Transfer learning lets you reach good accuracy on small datasets with free GPU time; learn to read loss curves and spot overfitting early.
- PyTorch
- CNNs
- Transformers
- Fine-tuning
Milestone project: Image classifier with transfer learning
Learn Deep Learning on BeeProton →MLOps
6 weeksTrack every experiment, version data and models together, and automate the path from new data to a tested, deployed model. Add monitoring for input drift and falling accuracy so problems surface before users notice.
- MLflow
- CI/CD for ML
- Model serving
- Monitoring & drift
Milestone project: Automated training pipeline
Cloud & scale
4 weeksRun your system on a cloud provider: choose between batch and real-time inference, size CPU or GPU machines for the load, and set budgets and alerts so experiments do not become surprise bills.
- AWS / GCP basics
- GPU inference
- Batch vs real-time
Milestone project: Deploy model on cloud with monitoring