Feb. 15, 2024, 5:34 p.m. | /u/slimsippin

Data Science www.reddit.com

I have experience in academia and from reading but not in industry. I only seen label shift during my internship but my internship ended before I could understand what was causing the positive label proportion to decline.

How do you folks in industry do root cause analysis of model performance decline? Is there some framework you use? How do you know when to retrain a model vs when there’s a bug in the pipeline? Any framework here would help truly …

academia analysis datascience experience industry internship people performance positive reading root cause analysis shift

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Principal Research Engineer - Materials

@ GKN Aerospace | Westlake, TX, US

Internship in Data and Projects

@ Bosch Group | Mechelen, Belgium

Research Scientist- Applied Mechanics

@ Corning | Pune, MH, IN, 410501

Product Data Analyst

@ AUTODOC | Lisbon-remote