Pkg.jl has been such a breath of fresh air vs my prior experience with Python,R, VBA. Built into language, environment management, dependency pinning, SemVer enforcement at the registry level, easy to update as a package developer.
Julia packaging scene has the unique advantage that it handles binary dependency (building and versioning) just like a regular Julia package. For example, CUDA.jl will "just work", and you don't need to manually install some specific version of CUDA for TensorFlow etc. which also makes reproducibility more robust. Where in Python the conda.yml doesn't work across OS/architecture, AND often doesn't track binary dependency (despite that's what Conda is designed to do...)