Databricks

I've read this article, which covers:Using CrossValidator or TrainValidationSplit to track hyperparameter tuning (no hyperopt). Only random/grid searchparallel "single-machine" model training with hyperopt using hyperopt.SparkTrials (not spark.ml)"Di...

AutoML presumably tries a few different algorithms while hyperparameter searching. What model types are considered?

I've seen .cache() and .checkpoint() used similarly in some workflows I've come across. What's the difference, and when should I use one over the other?

I'm a data scientist creating versioned ML models. For compliance reasons, I need to be able to replicate the training data for each model version. I've seen that you can version datasets by using delta, but the default retention period is around 30 ...