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Three Developments in AI Infra
Robert Nishihara
Co-creator of Ray, Co-founder of Anyscale
My Background
2013: Began PhD in AI
2016: Started Ray along with Philipp Moritz and Ion Stoica
2019: Started Anyscale to commercialize Ray
AI/ML Infra Meetup | RAYvolution - The Last Mile: Mastering AI Deployment with Ray
(π)
Physical Intelligence
01.AI
AI/ML Infra Meetup | RAYvolution - The Last Mile: Mastering AI Deployment with Ray
Three Industry Trends
How the world has changed since we started Ray
Three Industry Trends
Three Industry Trends
Scaling Laws
● Larger models, more compute, more data → Better results
● Top priority for many AI infra teams: “enable training on 100x more data”
● ML platform / infra teams are on the critical path
● Scaling data -> scaling compute
Three Industry Trends
Scaling Laws
● Larger models, more compute, more data → Better results
● Top priority for many AI infra teams: “enable training on 100x more data”
● ML platform / infra teams are on the critical path
● Scaling data -> scaling compute
Question: How can I use more compute to get
higher quality data?
Three Industry Trends
Scaling Laws
● Larger models, more compute, more data → Better results
● Top priority for many AI infra teams: “enable training on 100x more data”
● ML platform / infra teams are on the critical path
● Scaling data -> scaling compute
AI Data Processing
● Data processing: SQL centric → AI centric
● Multimodal data (text, images, video, audio, …)
● New requirements: data intensive and GPU intensive
Three Industry Trends
Scaling Laws
● Larger models, more compute, more data → Better results
● Top priority for many AI infra teams: “enable training on 100x more data”
● ML platform / infra teams are on the critical path
● Scaling data -> scaling compute
AI Data Processing
● Data processing: SQL centric → AI centric
● Multimodal data (text, images, video, audio, …)
● New requirements: data intensive and GPU intensive
Question: How can I use AI to get more insights from my data?
Three Industry Trends
Scaling Laws
● Larger models, more compute, more data → Better results
● Top priority for many AI infra teams: “enable training on 100x more data”
● ML platform / infra teams are on the critical path
● Scaling data -> scaling compute
AI Data Processing
● Data processing: SQL centric → AI centric
● Multimodal data (text, images, video, audio, …)
● New requirements: data intensive and GPU intensive
Heterogeneity
● Multimodality, hardware accelerators, frameworks, clouds, models
Three Industry Trends
Scaling Laws
● Larger models, more compute, more data → Better results
● Top priority for many AI infra teams: “enable training on 100x more data”
● ML platform / infra teams are on the critical path
● Scaling data -> scaling compute
AI Data Processing
● Data processing: SQL centric → AI centric
● Multimodal data (text, images, video, audio, …)
● New requirements: data intensive and GPU intensive
Heterogeneity
● Multimodality, hardware accelerators, frameworks, clouds, models
AI/ML Infra Meetup | RAYvolution - The Last Mile: Mastering AI Deployment with Ray
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