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Google has announced the general availability of its C4A virtual machines, marking the debut of Axion-based instances. The cloud provider claims these instances deliver up to 10% better price-performance compared to the latest Arm-based alternatives from competitors, including Amazon Graviton4.
Netflix extended its prioritized load-shedding implementation to the individual service level to further improve system resilience. The approach uses cloud capacity more efficiently by shedding low-priority requests only when necessary instead of maintaining separate clusters for failure isolation.
Wenjie Zi of Grammarly addressed the high failure rates in machine learning at QCon SF 2024, revealing challenges from misaligned business goals to poor data quality. She advocated for a "fail fast" approach and robust MLOps infrastructure, emphasizing that learning from failures can drive success. Clear objectives and rigorous practices are essential for effective implementation.
At QConSF 2024, Cody Yu presented how Anyscale’s Ray can more effectively handle scaling out batch inference. Some of the problems Ray can assist with include scaling large datasets (hundreds of GBs or more), ensuring reliability with spot and on-demand instances, managing multi-stage heterogeneous compute, and managing tradeoffs with cost and latency.
At QCon SF 2024, Faye Zhang gave a talk titled Search: from Linear to Multiverse, covering three trends and techniques in AI-powered search: multi-modal interaction, personalization, and simulation with AI agents.
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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First Google Axion Processor Now Available: Claims Best Performance in Cloud Market
Google has announced the general availability of its C4A virtual machines, marking the debut of Axion-based instances. The cloud provider claims these instances deliver up to 10% better price-performance compared to the latest Arm-based alternatives from competitors, including Amazon Graviton4.
By Renato LosioNetflix Rolls Out Service-Level Prioritized Load Shedding to Improve Resiliency
Netflix extended its prioritized load-shedding implementation to the individual service level to further improve system resilience. The approach uses cloud capacity more efficiently by shedding low-priority requests only when necessary instead of maintaining separate clusters for failure isolation.
By Rafal GancarzQCon SF 2024 - Why ML Projects Fail to Reach Production
Wenjie Zi of Grammarly addressed the high failure rates in machine learning at QCon SF 2024, revealing challenges from misaligned business goals to poor data quality. She advocated for a "fail fast" approach and robust MLOps infrastructure, emphasizing that learning from failures can drive success. Clear objectives and rigorous practices are essential for effective implementation.
By Andrew HoblitzellQCon SF 2024 - Scale out Batch GPU Inference with Ray
At QConSF 2024, Cody Yu presented how Anyscale’s Ray can more effectively handle scaling out batch inference. Some of the problems Ray can assist with include scaling large datasets (hundreds of GBs or more), ensuring reliability with spot and on-demand instances, managing multi-stage heterogeneous compute, and managing tradeoffs with cost and latency.
By Andrew HoblitzellTechniques and Trends in AI-Powered Search by Faye Zhang at QCon SF
At QCon SF 2024, Faye Zhang gave a talk titled Search: from Linear to Multiverse, covering three trends and techniques in AI-powered search: multi-modal interaction, personalization, and simulation with AI agents.
By Anthony Alford