Managing ML Models From Iteration To Production With MLOps In Snowflake
Ғылым және технология
Quick iteration with models running in production is the key to success. Snowflake’s Model Registry and MLOps features turn this process into a robust workflow for pushing models from development to production. This video shows how Snowflake can be used to manage lifecycle, access, and inferences of models.
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Пікірлер: 3
Great demo 👍 1. whats table size limit to train/infer/predict? I imagine Snowflake behind the scenes splits table rows and computes distributes to learn and to predict? Probably there's no hard limit in table size, but a doft limit to train multiple batches of rows, and repeat till full table trained right? Regarding prediction I suppose compute threads pick/predict row by row basis? or it depends on model type with some able to work in batches of rows? 2. finally you mentioned data set versioning? how does this physically maps to tables? is a version just a time travel timestamp (so we can reuse/access same data?) Thanks
@SumitDas-snow
19 күн бұрын
1. Train limit is based on your size of warehouse as data need to fit in the memory. No limit on infer/predict 2. Dataset is meant to be an immutable copy for the data for provenance / reproducibility. Table is mutable.
@emanueol
19 күн бұрын
ok thanks, and how about the meaning of dataset version shown in 26m 33s in videov name='AI307_DS.DATA.TRAINING_DATASET version='v2'