Setup
There are many online courses explaining different pieces of ML. Very few of them answer how to really apply ML in production problems. We share some best practices on how to build ML systems. All built with simple examples in Python but general enough for non-pythonists too.
Prepare Working Environment
Run make install - sets up virtual environment in venv directory and installs dependencies from requirements.txt
Run jupyter lab
Open default lab url
Open notebook
Editing Jupyter Book Export
jupyter-book clean book/
jupyter-book build book/
It is possible to setup entr to get the book rebuild with every new .md or .ipynb file saved with
find book -path book/_build -prune -false -o \( -name '*.ipynb' -o -name '*.md' \) | entr jupyter-book build book
