[lmcinnes]

If this is a thing you want to be able to do efficiently then ParametricUMAP (see [docs](https://umap-learn.readthedocs.io/en/latest/parametric_umap....) and [the paper](https://arxiv.org/abs/2009.12981)) will be very effective. It uses a neural network to learn a mapping directly from data to embedding space using a UMAP loss. Pushing new data through is only slightly more expensive than PCA, so being part of an inference pipeline is fine.

[nestorD]

But then you have to train a neural network and lose on the speed advantage of UMAP (offline yes but still much slower and finicky).

[lmcinnes]

It is really not that much slower for training (see the paper), and if you are interested in pipelines the difference is not so great considering you are looking at a one off training time vs. lots of inference.