Most ML projects do not fail because the model is wrong. They fail because the data pipeline feeding the model cannot survive contact with production. A notebook that trains a model on a clean CSV proves nothing about whether that model gets fresh, correct, timely data once real users and real ...
Source: [Dev.to](https://dev.to/eva_clari_289d85ecc68da48/designing-scalable-data-pipelines-for-machine-learning-applications-1boo)