Skip to main content

Supplementary Materials

Practical skills that every ML engineer needs but do not fit neatly into the other chapters.

  • Python Tips: Generators, context managers, dataclasses, type hints, performance
  • Linux and Shell: Essential commands, environment management, SSH, SLURM
  • Git for ML: Branching for experiments, git lfs, DVC