Pickles,

Oh Pickles,

How I need you.

To store finished models,

for my CPU.

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As Data Scientists, a key part of our workflow is generating models. More often than not, we are fitting multiple models to our data to find which one works best and providing analysis based on the results. This means that if we’re dealing with 30,000 rows and 100 columns of data — which isn’t atypical — modeling data may take a very long time. Furthermore, if we’re grid-searching to find the best hyperparameters for each model, the time it takes to fit a model and get results increases significantly. And there’s the problem, having to rerun those models over and over again each time you run code can be computationally expensive and waste a lot of time. …


Kwamae Delva

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