Proper engineering and infrastructure are crucial before deploying machine learning models effectively. Without the right tools and standardized evaluation methods, data scientists may struggle with reproducibility and consistency. Additionally, varying levels of machine learning expertise within teams can lead to misconceptions about its applicability, highlighting the need for collaboration and education.
