Exscientia 如何将监控和准备模型所需的时间从几天缩短到几小时
公司规模
Mid-size Company
地区
- Europe
国家
- United Kingdom
产品
- Seldon Deploy
技术栈
- AI
- Machine Learning
- MLOps
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Innovation Output
技术
- 分析与建模 - 机器学习
- 应用基础设施与中间件 - API 集成与管理
适用行业
- 药品
- 医疗保健和医院
适用功能
- 产品研发
用例
- 质量预测分析
- 机器状态监测
服务
- 数据科学服务
- 系统集成
关于客户
Exscientia plc 是一家由人工智能驱动的药物研发组织。该公司的使命是设计和开发新型、精密设计的药物，以提高临床成功率，造福患者。人工智能是其业务的核心，也是项目成败的决定性因素。该公司不断发展壮大，扩大了业务范围和目标，因此需要企业级规模解决方案。该公司独特的模型部署流程完全自动化，无需人工干预即可交付、监控和重新训练数千个模型。
挑战
Exscientia plc 是一家由人工智能驱动的药物研发组织，其模型的准确性和稳定性至关重要。该公司的模型部署流程非常独特，因为它是完全自动化的，因此无需人工干预即可交付、监控和重新训练数千个模型。然而，随着 Exscientia 扩大其覆盖范围和目标，它需要企业级规模的解决方案。该团队正在寻找额外的运营效率和其他方法来调试和稳定模型。现有的开源部署解决方案和推理平台已无法满足他们日益增长的需求。
解决方案
Exscientia 决定实施 Seldon Deploy 来协助监控和管理生产中的模型，使他们能够扩展并加快调试过程。团队必须决定在迁移到新的 MLOps 平台时是否要更改模型代码。更改代码可能会减慢整个项目的速度，因此他们决定保留相同的代码并将其移植到所有现有模型上。实施 Seldon 的好处之一是代码对 Seldon 来说并不重要，他们能够将架构与模型代码相匹配。这一决定节省了大量时间，并使 Exscientia 能够保持目前生产中的许多模型的稳定性和可靠性。
运营影响
数量效益
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