AccuWeather 的转型：使用 Azure 机器学习预测天气对业务的影响
技术
- 分析与建模 - 机器学习
- 应用基础设施与中间件 - 数据库管理和存储
适用行业
- 教育
- 设备与机械
适用功能
- 物流运输
- 销售与市场营销
用例
- 库存管理
- 预测性维护
服务
- 云规划/设计/实施服务
- 数据科学服务
关于客户
AccuWeather, Inc. 总部位于宾夕法尼亚州州立学院市，是全球领先的天气预报提供商。它为全球 230 万个地点提供未来 90 天内每小时和未来两小时内每分钟的 100 多个参数（包括温度、湿度、雨、雪和冰）的每分钟预报。 AccuWeather 从陆地、船舶、飞机、卫星和雷达收集数十万个实时天气观测数据，并使用其获得专利的 AccuWeather 预报引擎处理这些数据。超过 180,000 个网站、200 个电视台、900 个广播电台和 600 份报纸提供 AccuWeather 天气预报。全球总共有近 20 亿人依靠 AccuWeather 来帮助他们规划生活、保护他们的业务并保持安全。
挑战
AccuWeather 是全球领先的天气预报提供商，面临着为企业客户提供定制天气影响预测的挑战。企业需要的不仅仅是天气数据；他们想要预测天气将如何影响他们的运营。最初，AccuWeather 在临时协商的基础上提供这项服务，这是一项劳动密集型工作。客户将分享他们的销售历史数据，AccuWeather 将让数据科学家清理数据并创建自定义模型。然而，这个过程不可扩展。 AccuWeather 希望实现这项服务的自动化，允许客户在线上传数据并生成自动预测。然而，他们担心当时拥有的分析工具不允许他们提供高质量的预测。
解决方案
AccuWeather 求助于 Microsoft Azure 为企业客户构建自动化、可扩展的天气预报服务。他们选择 Azure 是因为其可靠的基础设施即服务产品，以及丰富且快速发展的平台即服务组合，包括大数据、机器学习和人工智能功能。 AccuWeather 首先将其 API 业务迁移到 Azure，后者提供了一种更具可扩展性、更具成本效益的服务管理方式。然后，他们使用 Azure Blob 存储、Azure 数据工厂和 Azure SQL 数据库等服务转移了大数据存储和处理。为了创建高度准确的预测，他们使用了 Azure 的机器学习工具，该工具功能强大，但可以使用 R 和 Python 代码进行自定义。 2017 年 5 月，AccuWeather 推出了 AccuWeather D3 Express，这是一款基于云的分析产品，可量化破坏性天气对业务的影响。然后他们开发了 D3 Advanced，它使用机器学习提供更多定制的预测。
运营影响
数量效益
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