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Running ML code on jupyter notebooks is good for quick prototyping and model exploration. On production level large scale model training settings we should prefer .py ML pipelines packed into a docker ...
0.0, 0.6, explore_wt=0.3, enforce_limits=True, sample_fn="uniform" "model.train_aug_stack.transforms.0.cd_rate": HyperParam( 0.01, 0.99, explore_wt=0.3, enforce ...
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