There are a huge number of new statistical, machine-learning and artificial intelligence solutions being released every month.
Most are open-source and written in a popular Python framework like TensorFlow, JAX, or PyTorch.
In order to 'guarantee' you are using the best [for given metric(s)] solution for your dataset, some way of automatically adding these new statistical, machine-learning and artificial intelligence solutions to your automated pipeline needs to be created.
(additionally: useful for testing your new optimiser, loss function, &etc. across a zoo of datasets)
Ditto for transfer learning models. A related problem is automatically putting ensemble networks together. Something like:
import some_broke_arch # pip install some_broke_arch import other_neat_arch # pip install other_neat_arch import horrible_v_arch # builtin to keras model = some_broke_arch.get_arch( **standard_arch_params ) metrics = other_neat_arch.get_metrics(**standard_metric_params) loss = horrible_v_arch.get_loss( **standard_loss_params ) model.compile(loss=loss, optimizer=keras.optimizers.RMSprop, metrics=metrics) print(model.summary()) # &etc.
In summary, I am petitioning for standard ways of:
To that end, I would recommend encouraging the PyPi folk to add a few new classifiers,
and a bunch of us trawl through GitHub every month sending PRs to
random repositories—associated with academic papers—linking up with
CI/CD so that they are now installable with
pip install and searchable by classifier on PyPi.
inspectmodules to traverse the module, class, and function hierarchy for 10 popular open-source ML/AI frameworks;