The Delphina Blog

Data science leaders must balance belief in the transformative power of ML & AI with reality: major data initiatives are risky and take months to move from conception to production.

Data science teams perennially face a burning — yet often unspoken — question: what drives high value actions?

Unlike many functions, the returns to quality are highly non-linear in ML — and mediocre ML is often downright dangerous. Unpack why, how to identify mediocre ML, and what to do about it.