Do you always get confused among concept drift vs data drift vs covariate drift like me? The diagram (from a research paper, https://arxiv.org/abs/1511.03816) provides a clear illustration of the different terms. In summary, concept drift in data refers to changes in environmental conditions that differ from the original environmental conditions under which a model is… Continue reading Concept drift vs data drift vs covariate drift
Month: October 2022
MLU-Explain : Visual explanation of ML concepts
A very cool website from Amazon that explains various machine learning concepts using interactive and visual essays. https://mlu-explain.github.io/ Using simple and interesting examples, the website really brings to life many core concepts in machine learning and makes them accessible to more people. This reminds me of how I learned physics during my high school era.… Continue reading MLU-Explain : Visual explanation of ML concepts
ML system design – problem definition & consulting
I recently started reading the excellent book called Designing Machine Learning Systems by Chip Huyen. In the first few chapters, the book illustrated very clearly the differing stakeholder expectations of an ML system, by using a restaurant recommendation app as an example. Data scientists / ML engineers ➡️ Want a model that recommends the best… Continue reading ML system design – problem definition & consulting
The happiness equation
(Image source: https://twitter.com/aurelien_gohier/status/1062248485154705408) We always tell ourselves that happiness will follow after we have worked hard and achieved great success. ”I will be very happy if I achieve the next milestone, be it a job promotion or buying a house or earning my first bucket of gold.” However often after we achieved those successes, happiness… Continue reading The happiness equation