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Versioning and Consistency in Model Training
This chapter emphasizes the significance of versioning in model training and the choice of consistent datasets while varying training parameters. It explores the use of metrics like perplexity to refine hyperparameters, addressing the challenges of maintaining effective machine learning practices.