← Back to Leaderboard
Project comparison
Awesome Production Machine Learning vs Ray
Compare adoption, momentum, maintenance health, and project basics before choosing which tool to evaluate deeper.
Awesome Production Machine Learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
20.5K
Stars
0
Weekly
43/100
Health
Ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
42.5K
Stars
+72
Weekly
79/100
Health
Quick Take
Adoption
Ray has the larger GitHub footprint with 42.5K stars.
Momentum
Ray is currently growing faster at +72 stars this week.
Maintenance
Ray has the stronger health score at 79/100.
Side-by-side Metrics
| Signal | Awesome Production Machine Learning | Ray |
|---|---|---|
| GitHub stars | 20.5K | 42.5K |
| Weekly growth | 0 | +72 |
| Health score | 43 | 79 |
| Contributors | 203 | 1.6K |
| Commits per week | 1.6 | 87.6 |
| Open issues | 22 | 3.5K |
| Language | N/A | Python |
| License | MIT | Apache-2.0 |
| Last commit | 23h ago | 15h ago |
| Last release | release-2026-05-01 | ray-2.55.1 |
Weekly AI open-source movers
Get the fastest-growing projects, useful MCP servers, and technical reads in one weekly email.