4 Key Aspects of a Data Science Project from a Data Science Leader

There is a tremendous amount of active research in making deep learning models interpretable (e.g., LIME and Layer wise Relevance Propagation). In summary, a high accuracy data science component by itself may not mean much even if it solves a pressing business need. On one extreme, it could be that the data science solution achieves high accuracy at the cost of high compute power or high turnaround time, neither of which are acceptable by the business. On the other extreme, it could be that the component that the end-user interacts with has minimal sensitivity to the errors of the data science component and thus a relatively simpler model would have sufficed the business needs. A good understanding of how the data science component fits into the overall end-to-end solution will undoubtedly help make the right design and implementation decisions.