Ray Summit 2022 stories - ML Platforms
Blog post from Anyscale
Uber's Michelangelo platform, built on top of Ray, achieved a 50% savings in ML compute costs for large-scale deep learning jobs by using a heterogeneous (CPU + GPU) cluster. The Uber team also experienced a 4x speedup in hyperparameter tuning jobs using Ray Tune. Spotify's ML team chose Ray due to its rich ML ecosystem integration and simplicity, eliminating the need to learn other frameworks or APIs. The team was able to democratize their platform, making it more accessible to employees from various backgrounds. Shopify's ML platform team built on top of open-source projects like Kubernetes and Ray, prioritizing scalability, fast iterations, and flexibility. By focusing on real use cases and user experience, the teams were able to successfully scale their ML workloads and deliver innovations with Ray.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Platform Engineering | 5 | 186 | 29 | 21 | +22% |
| Kubernetes | 2 | 1,328 | 195 | 77 | -5% |
| Reinforcement learning | 1 | No monthly metrics for this publish month. | |||
| Vector Search | 1 | 806 | 116 | 54 | +110% |
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