Organizations take inefficient ML approach
Each project takes too long to bring value
Bespoke solution are hard to maintain and bring tech debt
Organization can’t hire enough ML engineers
Introducing declarative ML system
higher abstraction, ease of use
open the door to non experts for ML
Pioneer project with Ludwig (Uber) and Overton (Apple)
How does Ludwig works? a configuration system with yaml
End to end deep learning architecture
Task flexibility
How to scale this concept and work with bigger amount of data?
Scalable backend over Ray
Doesn’t require you to provision heavy weighted infra, like a spark cluster, everything on the same layer
Predibase on top of Ludwig:
Take a look of the end-to-end problem of data flow in ML model to put it in production
Both batch and real-time production
Low code
Workflow
⇒ Check their paper about declarative ML