自动化数据科学：平民化

时间：2022-03-25 10:36:01 | 来源：行业动态

时间：2022-03-25 10:36:01 来源：行业动态

One of the chief benefits of AutoML 2.0 platforms is true data science democratization. When data science automation can accelerate and automate the process of discovering and creating features, it allows for a more diverse and abundant group of users to contribute to the data science process. Automation of feature creation allows the "citizen" data scientist to create incredibly useful, highly optimized use-cases. Because citizen data scientists typically have a high degree of "domain expertise," they can focus on use cases that are of high value to the organization with minimal if any assistance from the data science team. The added benefit of enabling citizen data scientists is that it allows the business to expand their use of data science without having to worry about hiring armies of data scientists. The ability to empower new data science contributors is especially significant given the difficulty organizations in the US have had in hiring data scientists, as examined in[ a 2018 LinkedIn study](https://news.linkedin.com/2018/8/linkedin-workforce- report-august-2018 ). With economic uncertainty facing the global community, enabling a new class of AI/ML developers with minimal investments becomes a game-changing value proposition to maintain or increase competitive advantages.

AutoML 2.0平台的主要好处之一是可以用于真正的数据科学平民化。

数据科学自动化可以加速发现要素和创建功能的过程，而且是自动的，如此一来，更多的用户群体就可以为数据科学过程做贡献。要素创建的自动化使得公民数据科学家能够创建极有用的、高度优化的用例。而且公民数据科学家通常具有高度的专业领域知识，因此他们基本无需数据科学团队的帮助就可以将重点放在对组织具有高价值的用例上。

开启公民数据科学家的另一个好处在于，企业无需担心招不到数据科学家而一样可以开拓数据科学的使用。2018年 LinkedIn的一项研究表明，美国的组织在雇用数据科学家方面遇到困难。鉴于此，能够发掘新的数据科学贡献者就显得尤为重要。

眼下，全球经济面临着诸多不确定性，在这种情况下能以最少的投资发掘出几类新的AI/ML开发人员，必将成为改变游戏规则的价值主张，在维持或增加竞争优势上意义重大。

**Automating Data Science: Productivity, Not Replacement**