Summary of Enabling More Efficient and Cost-effective Ai/ml Systems with Collective Mind, Virtualized Mlops, Mlperf, Collective Knowledge Playground and Reproducible Optimization Tournaments, by Grigori Fursin
Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments
by Grigori Fursin
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET); Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This educational initiative aims to optimize running AI, ML, and emerging workloads across diverse models, datasets, software, and hardware. The project utilizes Collective Mind (CM) virtualized MLOps and DevOps (CM4MLOps), MLPerf benchmarks, and the Collective Knowledge playground (CK), which was developed in collaboration with the community and MLCommons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This project makes it easier to run AI and machine learning workloads efficiently and cost-effectively. It uses special tools like Collective Mind, virtualized MLOps and DevOps, and benchmark tests from MLPerf. The goal is to make it easy for people to learn how to use these tools. |
Keywords
» Artificial intelligence » Machine learning
