activity
20152021
most citedCodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking

15 citations · 44 across the 7 of their papers we have counts for

collaborators

10 papers

cs.DC2021

Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development

Rafael Ferreira da Silva, Henri Casanova, Kyle Chard +55

Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflow…

cs.DC20211 cited

Workflows Community Summit: Bringing the Scientific Workflows Community Together

Rafael Ferreira da Silva, Henri Casanova, Kyle Chard +42

Scientific workflows have been used almost universally across scientific domains, and have underpinned some of the most significant discoveries of the past several decades. Many of…

cs.LG2020

Collective Knowledge: organizing research projects as a database of reusable components and portable workflows with common APIs

Grigori Fursin

This article provides the motivation and overview of the Collective Knowledge framework (CK or cKnowledge). The CK concept is to decompose research projects into reusable component…

cs.LG20202 cited

The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps

Grigori Fursin

This article provides an overview of the Collective Knowledge technology (CK or cKnowledge). CK attempts to make it easier to reproduce ML&systems research, deploy ML models in pro…

cs.LG202015 cited

CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking

Grigori Fursin, Herve Guillou, Nicolas Essayan

We present CodeReef - an open platform to share all the components necessary to enable cross-platform MLOps (MLSysOps), i.e. automating the deployment of ML models across diverse s…

cs.LG2019

MLSys: The New Frontier of Machine Learning Systems

Alexander Ratner, Dan Alistarh, Gustavo Alonso +66

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…