A model can score exceptionally well in a notebook and still fail on genuinely new data. One common reason is data leakage: information that would not be available at prediction time has influenced training or evaluation.
Tag: Data Science
Making Data Pipelines Reproducible: Version Inputs, Code, and Parameters
When a pipeline produces an unexpected result, the first questions are usually simple: Which data did it read? Which code ran? What parameters were used? If the answers are scattered across logs, shell history, and mutable object paths, reproducing the run becomes guesswork.