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- Make a decision around how much we want to do in the model vs how much we want to do in operation graphs?
- Come up with a consistent way to do padding (in the dataloader) so that we can use the same function for training, dataloading and serving
- Establish how we handle data transformations for small batches in a relatively standard way
- Dictionary/dataframe translation
- Optimizing Merlin operators for serving and dataloading (small batch)
- NVTabular - Simplify and standardize Categorify(), so that unique values parquet can be used for reverse categorify
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