4 papers
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…
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…
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…
"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…