activity
20202024
most citedOperationalizing Machine Learning: An Interview Study

29 citations · 57 across the 7 of their papers we have counts for

collaborators

7 papers

cs.DB2024

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…

cs.CE2024

Interactive Lab Notebooks for Robotics Researchers

Rolando Garcia

Interactive notebooks, such as Jupyter, have revolutionized the field of data science by providing an integrated environment for data, code, and documentation. However, their adopt…

cs.HC2024★ 23 cited

"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…

cs.SE2023

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…

cs.SE2022★ 29 cited

Operationalizing Machine Learning: An Interview Study

Shreya Shankar, Rolando Garcia, Joseph M. Hellerstein +1

Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, co…

cs.DC2021

BuDDI: A Declarative Bloom Language for CALM Programming

Rolando Garcia, Giulia Guidi

Coordination protocols help programmers of distributed systems reason about the effects of transactions on the state of the system, but they're not cheap. Coordination protocols ma…

cs.DC2020★ 5 cited

Hindsight Logging for Model Training

Rolando Garcia, Eric Liu, Vikram Sreekanti +5

In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experie…