Abstract: 

In this talk we discuss the fundamental differences between how data scientists and machine learning engineers approach the world, and how the resulting tension between them leads to one of the main barriers that must be overcome for ML projects to succeed. We describe how a machine learning platform like Kubeflow can dramatically change how these groups work and collaborate. We present several example scenarios in the lifecycle of a machine learning project, and show how an ML platform increases collaboration, reduces friction, and improves the lives of data scientists and ML engineers. Platforms, and the change in methodology that they enable, can dramatically transform culture, reduce risk, and help improve ML project outcomes.

Bio: 

Chase is a solutions architect at Arrikto with a passion for connecting people to technical solutions that can prevent them from wasting precious time and mental energy- solving the same problems over and over. Chase is a certified Kubernetes Administrator, Developer, and Security Specialist who works to help clients reduce MLOps friction and toil while ensuring the "non-negotiables" are enforced to provide the best return on their production models.