在 DemystData 使数据科学民主化

揭秘数据

DemystData始于 2010 年，当时他意识到，虽然世界上应该充斥着数据，但很少有数据被用于为客户谋取利益。 90% 的分析项目在构思、发现或部署阶段都失败了，并且很少实现价值。数据存在。分析是可能的。但生态系统很复杂。企业在获取价值方面面临越来越大的障碍。

DemystData 想要一种方法来处理与数据集越来越大和数据源越来越多样化相关的复杂性和时间密集型工作。他们的数据科学家花费大量时间手动构建数据科学和机器学习管道。

DemystData 旨在缩小这一差距，并通过向客户开放对新数据和更多数据的访问来帮助解决问题。但随着数据集变得越来越大，数据源越来越多样化，这也意味着这家总部位于纽约的公司本已有限的数据科学资源池的复杂性增加，工作也更加耗时。

使用 DataRobot 的自动化机器学习平台，DemystData 能够自动化和简化数据科学中更费力和耗时的部分，如特征工程、模型特征选择和模型部署，以提高各种机器学习项目的价值实现速度。

通过自动化机器学习生命周期中许多以前手动且耗时的步骤，DataRobot 能够帮助 DemystData 不仅提高其模型的质量，而且提高其整体数据科学生产力

DataRobot 不仅影响了 DemystData 的数据科学家；由于其简单易用，即使是没有数学或数据科学背景的 DemystData 工作人员现在也能够为机器学习项目做出贡献。

• DataRobot had a significant impact on DemystData's operations, not only improving the quality of their models and overall data science productivity, but also democratizing data science across the entire organization. The platform's simplicity and ease-of-use allowed even non-technical employees to contribute to machine learning projects, fostering a more inclusive and collaborative work environment. This also changed how DemystData approached hiring, as they could now focus on hiring for specific capabilities rather than generalist data scientists. Furthermore, DataRobot enabled DemystData to focus on their core competency of finding more data and identifying more signals, leading to more efficient use of resources and better business outcomes.

• DataRobot's platform was able to generate dozens of models in minutes, significantly reducing the time spent on modeling.
• DemystData was able to work at ten times the efficiency with DataRobot compared to their previous operations.
• DataRobot allowed DemystData to do the same amount of work with fewer people, leading to cost savings.