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Iguazio > 实例探究 > 提高金融机构 MLOps 效率
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HCI’s Journey to MLOps Efficiency: A Case Study

技术
  • 分析与建模 - 数据挖掘
  • 分析与建模 - 机器学习
适用行业
  • 矿业
  • 运输
适用功能
  • 物流运输
  • 维护
用例
  • 最后一英里交付
  • 时间敏感网络
服务
  • 测试与认证
  • 培训
挑战
尽管机器学习在金融机构中具有潜力,但由于交付时间长且数据访问有限,部署机器学习模型通常效率低下。
关于客户
HCI 是一家全球消费金融提供商,他们认识到机器学习模型在风险相关用例中的潜力。
解决方案
HCI 实施了多项措施来提高 ML 效率,包括构建数据策略、通过集成、标准化和自动化缩短交付时间,以及通过硬件、文件系统、维护支持、企业安全和数据管理提高运营和弹性。
运营影响
  • As a result of implementing these efficiency measures, HCI was able to significantly improve their ML operations. They were able to reduce the time to delivery by 3 to 6.6 times, and even up to 10 times in some cases. They also managed to cut operating costs by 60% and reduce storage capacity by 20 times. With the use of MLRun, they were able to run automated, fast, and continuous ML processes and deliver production data. Code was deployed to the microservice in one click, pipeline deployment was automated, and monitoring was automated and codeless. Through collaborative and continuous development and MLOps, HCI achieved faster time to production, efficient use of resources, high quality and responsible AI, and continuous application improvement.
数量效益
  • Time to delivery reduced by 3 to 6.6X and even up to 10x
  • Operating costs cut by 60%
  • Storage capacity reduced by 20X

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