Inside a presentation at QCon Moreover in 2023, Zhe Zang from Anyscale provided supplemental context on how companies like Pinterest use Ray for big-scale equipment-learning jobs. Zhe highlighted that Ray's versatility and ease of use help it become specially eye-catching for organisations wanting to modernise their ML infrastructure.
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DoorDash also confronted one of a kind difficulties with GPU accessibility in their Kubernetes environment, which necessary near collaboration with Nvidia to solve driver compatibility challenges.
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DoorDash described Gains comparable to These of Pinterest, which include enhanced velocity and suppleness. They minimized some time to just take an ML notion to production from weeks to a few days. In addition they saw important efficiency gains, with 10-20x enhancements for a few use cases migrated from their preceding system to Ray.
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The company also created a focused consumer interface for persistent logging and metrics. This UI permits log Evaluation while not having an Energetic Ray cluster, helping to mitigate expenses linked to idle resources like GPUs.