The Machine Learning Engineer trek
From linear models to large-scale training systems. Algorithms, deep learning, feature engineering, model evaluation, and production ML infrastructure.
Math & programming foundations
The mathematical foundations and Python proficiency that every ML engineer needs.
Classical ML algorithms
The algorithms behind the sklearn API — understanding them deeply makes you better at debugging and choosing.
Feature engineering & data preparation
The craft that often matters more than algorithm choice. Encoding, scaling, imputation, feature selection, and handling real-world data.
Model evaluation & selection
Beyond accuracy: rigorous evaluation that prevents you from shipping models that fail in production.
Deep learning with PyTorch
Neural network fundamentals, training from scratch, and the debugging skills that make you effective beyond running tutorials.
NLP & computer vision
Transfer learning with transformers for text and vision. Fine-tuning, zero-shot, and multimodal models.
Large-scale model training
Distributed training, mixed precision, gradient checkpointing, and training models that don't fit on one GPU.
Hyperparameter optimization at scale
Systematic, efficient hyperparameter search — from Bayesian optimization to neural architecture search.
MLOps & model lifecycle
Experiment tracking, model registry, feature stores, and the infrastructure that makes ML teams productive.
Model monitoring & drift detection
Keeping models accurate after they ship: data drift, concept drift, and the retraining triggers that matter.
ML system design
Designing ML systems for scale, reliability, and maintainability. The skills for senior ML engineering roles.
Research & paper implementation
Reading papers effectively, implementing ideas, and contributing to the field.
Capstone — end-to-end ML system
Build, train, deploy, and monitor a production ML system that solves a real problem at realistic scale.
Trek complete. What's next?
You've walked the full roadmap. Now ship the capstone, write about it, and share the path with the next engineer who needs it.