ML in Practice


People & Processes
Delivering machine learning based projects is often a team effort. In this part I'll be writing about some of my thinking how to best organize people and projects.
My main insight is that data science based work is different from "normal" engineering work because there is much higher amount of uncertainty that can require a lot of experimentation to figure out.
Other questions are how to best structure teams, and organizations. Introducing data science into an organization is a change process that is not just about hiring data scientists (which in itself is already a big challenge). You need to gain experience with data science, gain trust as an organization that there is some tangible return on investment, and gradually being to scale out data science across the whole organization.