AI In Production Key Challenges and Use Cases
summary
Robovision builds a no-code platform that treats each model as one ray actor and composes them as ML framework independent compute cells inside Docker/Kubernetes, enabling a singular Ray cluster per deployment with custom resources and conda environments. Real results: a traffic pipeline moves from 5 fps to 16 frames a second, a greenhouse customer creates 400+ models and robots plant 10,000 tulips/hour, edge builds run on NVIDIA Xavier/Jetson, and an AI store ships shareable models — all retrainable on-site and used across agritech, healthcare, manufacturing and smart cities.
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chapters (8)
- 0:00Introduction and company background
- 1:26Industrializing AI challenges
- 3:32Deep learning project fundamentals
- 8:09Scalable AI system practices
- 9:34Ray for high-throughput pipelines
- 13:26Scaling with AI labeling
- 16:13Robovision on Ray use cases
- 24:41AI at scale in production
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