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Iโm doing timeseries demand forecasting on ~10K products, with distinct behaviors. So one model per product.
In Spark Iโd just do a groupby(productID).apply(modelCode).
Whatโs an efficient to code, efficient to run way to do this in pandas in Dataiku?
Best to do partitioned model? The data is sitting on Snowflake, so for partitioning, do I cluster on productID? (Actually products are identified over 5 features).
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