Predicting Model Failures in Production

Abstract: 

ML Models in production lose accuracy over time. A number of factors contribute to this change, like Demographic Mix, Consumer Behavior change, etc. Using these models' output results in incorrect decisions that could lead to catastrophic failures for the organization. This existing whitespace calls for a solution(s) to help Data Science teams predict the failure in advance. We at Tredence have developed a suite of libraries which are able to predict model accuracy drop & trigger alerts to proactively fix the model. All of these libraries are packaged in the E2E model management accelerator – ML Works.

Bio: 

Aravind heads the Data Science Org at Tredence, and his team works on the R&D & algorithm development for new Data Science solutions

Open Data Science

Open Data Science
Innovation Center
101 Main St
Cambridge, MA 02142
info@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from Youtube
Vimeo
Consent to display content from Vimeo
Google Maps
Consent to display content from Google