Reproducibility
Explore how to maintain reproducibility in machine learning pipelines by understanding sources of randomness and changes such as data input, preprocessing, data splitting, model selection, and environment versions. Learn practical techniques like data versioning, setting random seeds, configuration control, and environment management to produce consistent model results across runs.
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Reproducibility is of paramount importance in science, and that’s also true when it comes to data science. A model trained on a given dataset a second time, with exactly the same preprocessing and feature engineering steps and hyperparameters, should perform almost—if not exactly—the same as the first model.
Traditional software programs are deterministic and, in general, will always output the same thing if the input is fixed. But ML systems are stochastic in nature, so this isn’t the case, and it takes some effort to achieve ...
