At Neptune, we get the chance to talk to many ML teams of different sizes, running projects in different ML areas. We often hear that it’s not easy for them to find frameworks and best practices around managing, versioning, and organizing experiments. So in this talk, we will share some insights and best practices that we learned from those teams – including what, why, and how they track in their ML projects.
Data Scientist, Neptune AI
Parth is a Data Scientist and a Developer Advocate at Neptune. Before joining Neptune, he assisted several startups in deploying scalable end-to-end machine/deep learning solutions to production. He also designed architectures for better experiment tracking and for defining machine learning life cycles in the cloud.