training data set: where do we get a training data set, and how similar/different is that training data set to things we see in ZTF? Can we boost our results by doing transfer learning (learning on multiple existing data sets, each with a mapping from their feature space into the ZTF feature space)?
features: What are good features? How do we define/evaluate what a "good" feature is?
imbalanced class membership: there are likely transients that appear often, and others that are rare. This changes how we need to think about our classification objective, for example if the rare transients are more valuable to find
uneven sampling of time series: always trouble
instrumental effects: Are there instrumental effects that might affect the shape of the time series?
Symmetries: Kyle Cranmer + colleagues are exploring ML algorithms that respect existing symmetries in the problem. Might be worth thinking about that.
Things we need to be aware of or worry about:
Will need to add more as we think of them.