Subject archive for "models," page 4

Humans and Machines: SciFi or Already Commonplace?

In this article we explain how we’re using Kubernetes to enable data scientists to deploy predictive models as production-grade APIs.

This is a Data Science Popup session by Hristo Spassimirov Paskov, Founder & CEO of ThinkFast.

Imagine you are a medical professional who is training a classifier to detect whether an individual has an extremely rare disease. You train your classifier, and it yields 99.9% accuracy on your test set. You're overcome with joy by these results, but when you check the labels outputted by the classifier, you see it always outputted "No Disease," regardless of the patient data. What's going on?!

It's been said that debugging is harder than programming. If we, as data scientists, are developing models ("programming") at the limits of our understanding, then we're probably not smart enough to validate those models (“debug”) effectively.

Data science is no longer a specialization of a single person or small group. It is now a key source of competitive advantage, and as a result, the scale of projects continues to grow. Collaboration is critical because it enables teams to take on larger problems than any individual. It also allows for specialization and a shared context that reduces dependency on "unicorn" employees who don't scale and are a major source of key-man risk. The problem is that collaboration is a vague term that blurs multiple concepts and best practices. In this post, we clarify the differences between repeatability, reproducibility, and whenever possible the golden standard of replicability. By establishing best practices of frictionless in-team and cross-team collaboration, you can dramatically improve the efficiency and impact of your data science efforts.