In this demo, we address some of the challenges faced by ML and Software Teams when it comes to collaborating on delivering ML powered applications. Some of these challenges include
The discrepancy in the frequency with which each of these teams need to iterate on their codebases and CI/CD pipelines
The fact that only a single set of experiment assets from an ML experimentation pipeline is relevant to the application
The challenge of syncing a model or other experiment assets across independent codebases.
Using the GitLab DevOps platform and Comet, we can start bridging the gap between ML and Software Engineering teams over the course of a project.
Get in touch with Sales: http://bit.ly/2IygR7z
Comet is an MLOps Platform that is designed to help Data Scientists and Teams build better models faster! Comet provides tooling to Track, Explain, Manage, and Monitor your models in a single place
https://www.comet.ml/site/
https://gitlab.com/tech-marketing/dev...