This report delves into the intricacies of the end-to-end data science workflow, highlighting the critical skill sets required domain expertise, data science, and data engineering-and the frequent communication and workflow breakdowns that slow progress. We explore the complexity of data preparation and deployment and how it impacts the efficiency of model refinement and deployment.

By examining these obstacles, we aim to provide actionable insights and strategies for overcoming delays, ultimately enabling faster and more effective AI/ML outcomes for the business.