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Provectus > 实例探究 > FireworkTV:利用高效的机器学习基础设施改进视频推荐
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FireworkTV's Infrastructure Overhaul: Enhancing Video Recommendation System with AWS

技术
  • 分析与建模 - 机器学习
  • 传感器 - 相机/视频系统
适用行业
  • 水泥
  • 建筑与基础设施
适用功能
  • 质量保证
用例
  • 施工管理
  • 基础设施检查
服务
  • 系统集成
  • 培训
挑战
FireworkTV 的 ML 团队认识到其现有 ML 基础设施的局限性(生产力落后、管理成本不断增加、缺乏自动化),并寻求在 AWS 上构建新的、更高效的基础设施,以推动推荐模型性能、质量和可靠性的改进。
关于客户
FireworkTV是世界上第一个去中心化的短视频网络。它通过策划根据每个人独特的生活方式和品味量身定制的 30 秒互动视频,将创作者、粉丝和参与的观众联系起来。
解决方案
Provectus 与 FireworkTV 的 ML 团队密切合作,审查了基础设施和推理流程,以使用 Amazon SageMaker 构建新的 ML 基础设施。推理和训练管道已迁移到 Amazon SageMaker,以实现大规模部署。
运营影响
  • The new ML infrastructure built on AWS has provided FireworkTV with a robust foundation for its video recommender system. The migration to Amazon SageMaker has circumvented the limitations of Lambda, reducing admin overhead and infrastructure costs. It has also empowered the ML team with a more efficient process and better collaboration. With the 2x reduction in ML infrastructure costs and 10x acceleration in inference and training pipelines, FireworkTV is now poised to scale and grow its video recommender system. The company can make faster improvements to its system, deliver personalized video recommendations in real time due to reduced latency, and is set for future growth through faster and more accurate video recommendations that engage users, increase app usage, and drive ad revenue.

数量效益
  • Reduced ML infrastructure costs by 2x

  • Sped up inferences by 10x

  • Built a new ML infrastructure on AWS in just four weeks

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