加速商业房地产洞察平台的机器学习模型生产
技术
- 分析与建模 - 机器学习
- 应用基础设施与中间件 - 中间件、SDK 和库
适用行业
- 水泥
- 教育
适用功能
- 物流运输
用例
- 最后一英里交付
- 租赁金融自动化
服务
- 数据科学服务
- 培训
关于客户
VTS 是一家商业房地产租赁和资产管理软件和数据公司。 VTS 由经历过当今房东和经纪人面临的挑战的房地产专业人士创建，提供易于使用、直观的工作流程和见解平台，使商业房地产专业人士能够更聪明地工作，而不是更辛苦。 VTS 平台管理着超过 120 亿平方英尺的商业地产。
挑战
VTS 希望更高效地生产机器学习 (ML) 模型，同时获得使用 AWS 服务迭代构建新模型的能力。他们希望加快 ML 应用程序的上市时间、减少人为错误以及 DS 团队的努力。 VTS 在将预测模型集成到核心用户体验中时遇到了障碍。数据科学家能够在临时环境（例如 Jupyter 笔记本）中交付模型，但发现使用 VTS 平台的现有基础设施在生产中部署模型具有挑战性。 VTS 拥有出色的数据科学家，但缺乏完成这项工作所需的 AWS 和 MLOps 专业知识。
解决方案
Provectus 研究了如何在 VTS 上对 ML 模型进行原型设计和评估，并提供了基于模板的解决方案，使 VTS 数据科学家能够更轻松地创建 Amazon SageMaker 作业、管道、终端节点和其他 AWS 资源。由此产生的一套连贯的模板，以及使用指南和扩展指南，已成功应用于预测租赁交易结果的机器学习模型。 Provectus 建议数据科学家可以使用 VTS 特定的模板和 SDK 在 Amazon SageMaker 和其他 AWS 服务中创建作业、管道和终端节点。模板和 SDK 将由 Provectus 设计、构建和交付，VTS 数据科学和机器学习工程团队将接受有关如何使用模板和 SDK 的广泛培训和指南。
运营影响
数量效益
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