Description
Machine learning (ML) solutions are becoming ubiquitous when tackling challenging problems, enabling end-users to access reliable, insightful information. However, many components of these solutions rely on domains outside traditional data science — e.g., data, DevOps, and software engineering.
In this talk, I'll walk through an end-to-end ML solution we built for transplant centers to identify likely stem cell donors. We'll then focus on how interacting with domains outside traditional data science can immensely help a project succeed and increase your impact.
You will take away specific examples of why thinking end-to-end can enhance your ML solutions and how to start applying these principles in your own organization.