AutoML and [Partially] automated feature engineering have hyperparameters too. Some algorithms have no hyperparameters. And, OT did a complete grid search instead of a PSO or gradient descent, for which there are also adversarial cases.<p>Featuretools supports Dask EntitySets for larger-than-RAM feature matrices, or pandas on multiple cores: <a href="https://featuretools.alteryx.com/en/stable/guides/using_dask_entitysets.html" rel="nofollow">https://featuretools.alteryx.com/en/stable/guides/using_dask...</a><p>"Hyperparameter optimization with Dask":
<a href="https://examples.dask.org/machine-learning/hyperparam-opt.html" rel="nofollow">https://examples.dask.org/machine-learning/hyperparam-opt.ht...</a> :<p>> <i>HyperbandSearchCV is Dask-ML’s meta-estimator to find the best hyperparameters. It can be used as an alternative to RandomizedSearchCV to find similar hyper-parameters in less time by not wasting time on hyper-parameters that are not promising. Specifically, it is almost guaranteed that it will find high performing models with minimal training.</i><p>Note that e.g. TabPFN is faster or converges more quickly than xgboost and other gradient boosting <i>with hyperparameter</i> methods: <a href="https://news.ycombinator.com/item?id=37269376#37274671">https://news.ycombinator.com/item?id=37269376#37274671</a><p>"Stochastic gradient descent written in SQL" (2023) <a href="https://news.ycombinator.com/item?id=35063522">https://news.ycombinator.com/item?id=35063522</a> :<p>> <i>What are some</i> adversarial <i>cases for gradient descent, and/or what sort of e.g. DVC.org or W3C PROV provenance information should be tracked for a production ML workflow?</i>