自动化数据科学：生产力而非替代

时间：2022-03-25 11:15:02 | 来源：行业动态

时间：2022-03-25 11:15:02 来源：行业动态

Any conversation of AutoML 2.0 platforms, however, is misplaced if the focus is on replacing or displacing the data scientist. Most data scientists see feature-engineering as one of the most significant obstacles to their work. Automation can only help to accelerate the process by providing incredible productivity boosts that would not be otherwise possible without automation. By leveraging AutoML 2.0, data scientists can often accelerate their work dramatically - from months to days. Besides, the use of AI-based feature engineering in AutoML 2.0 platforms, allows data scientists to discover features that they would have never considered. AI-based feature engineering automatically builds, evaluates, and exposes features by combining data from multiple columns, often across different tables and sources. The ability of AutoML 2.0 to self-discover features allows data scientists to explore the so- called "unknown unknowns," the features the data scientists would have never even considered because of either lack of time or lack of domain expertise.

但任何AutoML 2.0平台如果将定位的重点放在替换或更替数据科学家上就大错特错了。大多数数据科学家都将要素工程视为工作中的最大障碍之一。自动化可以帮助加快要素工程的流程，靠的就是自动化可以提供令人难以置信的生产率提升，这种提升若无自动化是不可能实现的。

对于数据科学家来说，利用AutoML 2.0通常可以极大地加快自己的工作，缩短的工作时间从几天到几个月不等。而且，数据科学家在AutoML 2.0平台上使用基于AI的要素工程还可以发现他们从未考虑过的要素。基于AI的要素工程可以自动构建、评估和开通要素，而且可以结合来自基于多列的数据（通常是跨越不同的表和源）。

此外，AutoML 2.0还具有自我发现要素的功能，数据科学家借此功能可以探索所谓的未知的未知数，这种未知的未知数属于那些数据科学家由于缺乏时间或缺乏领域专业知识而从未考虑过的要素。

**AutoML 2.0: Creating A More Productive, More Inclusive AI/ML Program**