Subject archive for "models," page 4


Humans in the Loop

Humans and Machines: SciFi or Already Commonplace?

By Paco Nathan9 min read

Data Science

Model Deployment Powered by Kubernetes

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

By Alexandre Bergeron7 min read

Data Science

Scaling Machine Learning to Modern Demands

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

By Grigoriy34 min read

Data Science

Sampling Based Methods for Class Imbalance in Datasets

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?!

By Manojit Nandi11 min read

Data Science

Benchmarking Predictive Models

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.

By Eduardo Ariño de la Rubia13 min read

Data Science

Principles of Collaboration in Data Science

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.

By Eduardo Ariño de la Rubia17 min read

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