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DataRobot
概述
公司介绍
DataRobot 为所有技能水平的数据科学家提供了一个机器学习平台，以在过去的一小部分时间内构建和部署准确的预测模型。该技术通过改变预测分析的速度和经济性来解决数据科学家的严重短缺问题。DataRobot 平台使用大规模并行处理来训练和评估 R、Python、Spark MLlib、H2O 和其他开源库中的 1000 个模型。它搜索数百万种可能的算法、预处理步骤、特征、转换和调整参数组合，为您的数据集和预测目标提供最佳模型。 DataRobot 平台评估数百种尖端机器学习算法，以发现、部署和定制适合各种情况的最佳机器学习模型。它还大规模提供最准确的见解，为各种规模的组织提供通往数据科学成功的最快途径。 DataRobot成立于2012年6月，总部位于美国马萨诸塞州波士顿。
物联网解决方案
自动化机器学习自动创建融合了我们世界一流的数据科学专业知识的先进机器学习模型。自动化时间序列自动开发复杂的时间序列模型，根据数据序列的历史和趋势预测数据序列的未来值。各种规模的组织都将改进对销售量、按 SKU 的产品需求、人员配备、库存和大量财务应用程序的预测。 MLOps提供数据科学和 IT Ops 团队合作部署、监控和管理生产中的机器学习模型所需的功能。
主要客户
埃森哲、Aegon、德勤、哈佛商学院、松下、联想、联合航空
物联网应用简介
技术栈
DataRobot的技术栈描绘了DataRobot在基础设施即服务 (iaas), 平台即服务 (paas), 和 分析与建模等物联网技术方面的实践。
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设备层
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边缘层
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云层
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应用层
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配套技术
技术能力:
无
弱
中等
强
实例探究.
Case Study
Demystifying Data Science: A Case Study on DemystData and DataRobot
DemystData, a New York-based software company, is dedicated to demystifying data for its clients, particularly financial institutions. Despite the increasing use of data in the financial sector, it is still heavily underutilized, leading to business decisions being made based on suboptimal or incomplete data. DemystData aims to close this gap by providing clients with access to new and more data. However, as datasets grow larger and data sources become more varied, the complexity increases, leading to more time-consuming work for the limited pool of data science resources at the company. The challenge was to manage this increasing complexity and workload without compromising the quality of data analysis and insights.
Case Study
Accelerates Data Discovery, Testing, and Deployment
As datasets get bigger and data sources more varied, complexity increases and work processes become more time-consuming. Demyst clients need help identifying which external data attributes are predictive in marketing, risk, and portfolio management use cases across the vast ocean of external data.
Case Study
84.51° Enhances Personalized Shopping Experience for Kroger Shoppers with DataRobot
84.51°, a retail data science, insights, and media company, was faced with the challenge of creating more personalized and valuable experiences for shoppers across the path to purchase. This included the entire customer journey, from initial awareness to activation, retention, and beyond. The company's goal was to leverage 1st-party retail data from over 62 million U.S. households to fuel a more customer-centric journey. However, the sheer volume of data and the complexity of creating personalized experiences for such a large customer base presented a significant challenge. The company needed a solution that would enable better production, deployment, and interpretation of data science models to meet its objectives.
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