Experimental Reproducibility in Data Science with Sacred
Experimental Reproducibility in Data Science with Sacred

Abstract: 

There are ways to incorporate experimental reproducibility into machine learning projects that are clean and lightweight. In this introductory level workshop, we demonstrate how to use Sacred to motivate reproducible research and experiment monitoring in machine learning. We discuss how this enables any data scientist to provide a solution (a model or set of predictions) to any problem, compare their solution to previous models results on the same test data, and select the best model for production. Finally, we provide examples of machine learning problems in retail and demonstrate how data scientists can easily work across multiple problems.

Bio: 

Karthik is a Data Scientist at Gilt working on predicting customer demand and lifetime value. Karthik has a background in mathematics, and worked a few years abroad in Shanghai working on machine learning problems in edtech and fintech.

Open Data Science

 

 

 

Open Data Science
One Broadway
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