marcellodb

7 ms·

1.
by marcellodb 11mo ago
I think "agents layered on top of AutoML" is a reasonable simple mental model for Plexe's model building capabilities, but it also masks some important qualitative differences between Plexe and traditional AutoML tools: 1. Au
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by marcellodb 11mo ago
It's a combination of tokens consumed, dataset + model storage cost, and inference + training compute cost.
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by marcellodb 11mo ago
Thanks! Great set of questions: 1. Tabular data only, for now. Text/images also work if they're in a table, but unfortunately not unstructured text or folders of loose image files. Full support for images, video, audio etc coming
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by marcellodb 11mo ago
Thanks for the great feedback! To your points: 1. Depending on your dataset the training could take from 45 mins to a few hours. We do need add an ETA on the build in the UI. 2. The input schema is inferred towards the end of the model buil
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by marcellodb 11mo ago
Unfortunately we don't officially support image, video or audio yet - only tabular data for now. We do plan to add that capability at some point in the coming weeks depending on popular demand. Do you have any particular use case in mi
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by marcellodb 11mo ago
Great question, this is super important. The agents in the platform have the ability to do some degree of cleaning on your data when building a model (for example, imputing missing values). However, major improvements to data quality are ge
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by marcellodb 11mo ago
Great question, and yes, it's quite different: Plexe generates code for a pipeline that processes your dataset (analysis, feature engineering, etc) and trains a custom ML model for your use case. When you call `.predict()`, it is that
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by marcellodb 11mo ago
Thanks, we're pretty opinionated on "this should make sense to non-ML practitioners" being a defining aspect of the product vision. Behind the scenes, we've had quite a few conversations specifically about how to avoid f