Bulletin of ANPA

Abstract submitted to ANPA Conference July 14–16, 2023

Volume 5, Number 1

Data Science, Quantum Computing, Artificial Intelligence

Abstract ID: ANPA2023-N00075

Abstract:

ANPA2023-N00075: Building end-to-end machine learning pipeline to predict tire attributes using Kubeflow

Authors:

• Shree Bhattarai; Torqata Data and Analytics

When a customer visits a tire shop to purchase/replace tire, the most important factors that come into play are whether (i) the size of the tire fits their vehicle? (ii) the tire design suits the surface they drive on? (Smooth road, Rough/Muddy road etc.), and (iii) it is suitable for the season (Winter, Summer etc.). This information is not readily available for all tires. Thus, our goal here was to build a ML model that would collect information from web search to build a classification model to extract these attributes. During this presentation, we will be talking about our approach in designing a ML pipeline utilizing KubeFlow in Google Cloud Platform to predict above three tire attributes. We will further explain how KubeFlow helps making deployment of machine learning workflows on containerized applications simple, portable and scalable.

To cite this abstract, use the following reference: https://anpaglobal.org/conference/2023/ANPA2023-N00075