Subject archive for "models," page 4
What Your CIO Needs to Know about Data Science
What would you rather be doing? Data science or DevOps?
By Domino4 min read
Humans in the Loop
Humans and Machines: SciFi or Already Commonplace?
By Paco Nathan9 min read
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
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
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
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
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