Text

How to Develop Data Science Projects for Production Deployments - Project Summary

About this Digital Document

The most common sources of user errors with data science models and how to prevent them

This project outlines the most common sources of errors users face with data science models, which includes when a model returns wrong outputs, varies output by release, crashes, misses the latest features, and doesn't run in a new environment. Solutions to these issues are demonstrated with coding examples.

Full Title
How to Develop Data Science Projects for Production Deployments - Project Summary
Member of
Lehigh AI
Contributor(s)
Creator: Kelley, Shannon
Date Issued
2024-11-26
Language
English
Type
Text
Genre
summaries
Form
electronic documents
Department name
Industrial and Systems Engineering
Media type
application/pdf
Keywords
data model
Unit Test
Version Control
Virtual Environments
Container
API
Web Service
Package
CI/CD
Access

https://creativecommons.org/licenses/by-nc/4.0/

Kelley, . S. (2024). How to Develop Data Science Projects for Production Deployments - Project Summary (1–). https://preserve.lehigh.edu/lehigh-scholarship/prize-winning-papers-posters/lehigh-ai-project-award/how-develop-data-science
Kelley, Shannon. 2024. “How to Develop Data Science Projects for Production Deployments - Project Summary”. https://preserve.lehigh.edu/lehigh-scholarship/prize-winning-papers-posters/lehigh-ai-project-award/how-develop-data-science.
Kelley, Shannon. How to Develop Data Science Projects for Production Deployments - Project Summary. 26 Nov. 2024, https://preserve.lehigh.edu/lehigh-scholarship/prize-winning-papers-posters/lehigh-ai-project-award/how-develop-data-science.