23 citations · 26 across the 13 of their papers we have counts for
5 papers · 1 filter
Flo: a Semantic Foundation for Progressive Stream Processing
Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein +1
Streaming systems are present throughout modern applications, processing continuous data in real-time. Existing streaming languages have a variety of semantic models and guarantees…
Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle
Rolando Garcia, Pragya Kallanagoudar, Chithra Anand +4
In this paper we present techniques to incrementally harvest and query arbitrary metadata from machine learning pipelines, without disrupting agile practices. We center our approac…
Suki: Choreographed Distributed Dataflow in Rust
Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein
Programming models for distributed dataflow have long focused on analytical workloads that allow the runtime to dynamically place and schedule compute logic. Meanwhile, models that…
Optimizing Distributed Protocols with Query Rewrites [Technical Report]
David Chu, Rithvik Panchapakesan, Shadaj Laddad +6
Distributed protocols such as 2PC and Paxos lie at the core of many systems in the cloud, but standard implementations do not scale. New scalable distributed protocols are develope…
"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning
Shreya Shankar, Rolando Garcia, Joseph M Hellerstein +1
Organizations rely on machine learning engineers (MLEs) to deploy models and maintain ML pipelines in production. Due to models' extensive reliance on fresh data, the operationaliz…