Train models here, now, in this tab.
Not a video, not a mockup. Real scikit-learn, compiled to WebAssembly, training on real data with no backend. Pick a dataset and the browser runs a full cross-validated leaderboard on your own machine - the same one BreezeML would.
The kernel boots on your first pick (about 10 MB, cached after). Then eight real scikit-learn classifiers train under identical 5-fold cross-validation, right here - no server sees your click.
The kernel loads scikit-learn, pandas, and numpy - exactly three of BreezeML's four core dependencies. That deliberate four-dependency contract is the whole reason the real library can run client-side at all. No fifth dependency, ever, or a CI test fails the build.
Read the dependency contract
pip install breezeml
from breezeml import datasets, classifiers
# the exact leaderboard you just watched
classifiers.compare(datasets.iris(), "species")