State of the art end-to-end machine learning lifecycle
Presenter Type
UNO Graduate Student (Masters)
Major/Field of Study
Information Systems and Quantitative Analysis
Advisor Information
Dr Yonas Kassa, Research Associate
Location
MBSC Ballroom Poster # 703 - G (Masters)
Presentation Type
Poster
Start Date
24-3-2023 10:30 AM
End Date
24-3-2023 11:45 AM
Abstract
This presentation will present review on standard platforms for end-to-end machine learning pipeline building including how to train different state of the art ML models, that involve model experimentation, reproducibility, and deployment. Will highlight how to measure their performances, and visually compare between machine learning models.
Scheduling
10:45 a.m.-Noon
State of the art end-to-end machine learning lifecycle
MBSC Ballroom Poster # 703 - G (Masters)
This presentation will present review on standard platforms for end-to-end machine learning pipeline building including how to train different state of the art ML models, that involve model experimentation, reproducibility, and deployment. Will highlight how to measure their performances, and visually compare between machine learning models.