173 citations · 337 across the 19 of their papers we have counts for
8 papers · 1 filter
Framing AI System Benchmarking as a Learning Task: FlexBench and the Open MLPerf Dataset
Grigori Fursin, Daniel Altunay
Existing AI system benchmarks such as MLPerf often struggle to keep pace with the rapidly evolving AI landscape, making it difficult to support informed deployment, optimization, a…
Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments
Grigori Fursin
This white paper introduces my educational community initiative to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
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…
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…
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…
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…
SysML'19 demo: customizable and reusable Collective Knowledge pipelines to automate and reproduce machine learning experiments
Grigori Fursin
Reproducing, comparing and reusing results from machine learning and systems papers is a very tedious, ad hoc and time-consuming process. I will demonstrate how to automate this pr…