23 citations · 26 across the 13 of their papers we have counts for
4 papers · 1 filter
Multiversion Hindsight Logging for Continuous Training
Rolando Garcia, Anusha Dandamudi, Gabriel Matute +4
Production Machine Learning involves continuous training: hosting multiple versions of models over time, often with many model versions running at once. When model performance does…
Optimizing the cloud? Don't train models. Build oracles!
Tiemo Bang, Conor Power, Siavash Ameli +2
We propose cloud oracles, an alternative to machine learning for online optimization of cloud configurations. Our cloud oracle approach guarantees complete accuracy and explainabil…
Optimizing Stateful Dataflow with Local Rewrites
Shadaj Laddad, Conor Power, Tyler Hou +2
Optimizing a stateful dataflow language is a challenging task. There are strict correctness constraints for preserving properties expected by downstream consumers, a large space of…
Invited Paper: Initial Steps Toward a Compiler for Distributed Programs
Joseph M. Hellerstein, Shadaj Laddad, Mae Milano +2
In the Hydro project we are designing a compiler toolkit that can optimize for the concerns of distributed systems, including scale-up and scale-down, availability, and consistency…