Published at LXer:

Model construction and training are just a small part of supporting machine learning (ML) workflows. Other things you need to address include porting your data to an accessible format and location; data cleaning and feature engineering; analyzing your trained models; managing model versioning; scalably serving your trained models; and avoiding training/serving skew. This is particularly the case when the workflows need to be portable and consistently repeatable and have many moving parts that need to be integrated.read more

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