AutoML 2.0必將令數(shù)據(jù)科學(xué)更加自動化

時間：2022-03-25 11:09:01 | 來源：行業(yè)動態(tài)

時間：2022-03-25 11:09:01 來源：行業(yè)動態(tài)

First-generation AutoML platforms have focused on automating the machine learning part of the data science process. In a traditional data science workflow, however, the longest and most challenging part is the highly manual step known as feature engineering. Feature engineering involves connecting data sources and building a flat "feature table" with a rich, diverse set of "features" that is evaluated against multiple Machine Learning algorithms. The challenge of feature engineering is that it requires an elevated level of domain expertise to ideate new features and is very iterative as features are evaluated and rejected or chosen. New platforms, however, have recently emerged that provide additional capabilities and automation aimed at solving this challenge. Platforms with "Automated Feature Engineering" capabilities now allow for the automated creation of feature-tables from relational data sources as well as flat files. This ability to "auto-generate" features in the data science process is a game-changing capability. Suddenly, the "citizen" data scientists - Business Intelligence (BI) analysts, data engineers, and other technically savvy members of the organization with deep domain knowledge - can become valuable contributors to an organization's development of ML and AI models. Through Automated Feature Engineering, BI teams can suddenly develop sophisticated predictive analytics algorithms in days, significantly accelerating their productivity with minimal help from data scientists.

第一代AutoML平臺的重點(diǎn)主要放在自動化數(shù)據(jù)科學(xué)過程中的機(jī)器學(xué)習(xí)部分。但在傳統(tǒng)的數(shù)據(jù)科學(xué)工作流程里，最冗長和最具挑戰(zhàn)性的部分則是被稱之為是要素工程的部分，要素工程是高度手動的一步，主要涉及到連接數(shù)據(jù)源及構(gòu)建寬大的要素表，需包含豐富多樣的要素。與此同時，這些要素還需要針對多種機(jī)器學(xué)習(xí)算法進(jìn)行評估。

目前，要素工程面臨的挑戰(zhàn)是，只有用更高水平領(lǐng)域的專業(yè)知識才能醞釀新的要素，而且這一過程需要在評估、拒絕或選擇要素時反復(fù)地做。但最近業(yè)界出現(xiàn)了新平臺，這些新平臺可以提供旨在解決這一挑戰(zhàn)的附加功能及自動化功能?，F(xiàn)在一些具有自動要素工程功能的平臺可以從關(guān)系數(shù)據(jù)源以及無結(jié)構(gòu)文件里自動創(chuàng)建要素表。這種能夠在數(shù)據(jù)科學(xué)過程中自動生成要素的方法，可以說是個改變游戲規(guī)則的功能。

于是，突然之間，公民數(shù)據(jù)科學(xué)家開始成為組織開發(fā)ML和AI模型的有價值貢獻(xiàn)者。一般來說，「公民數(shù)據(jù)科學(xué)家」指的是商業(yè)智能（BI）分析師、數(shù)據(jù)工程師和組織中其他具有深厚領(lǐng)域知識的、精通技術(shù)的成員。借助于機(jī)器學(xué)習(xí)，BI團(tuán)隊(duì)利用自動化要素工程可以在幾天之內(nèi)開發(fā)出復(fù)雜的預(yù)測分析算法，無需數(shù)據(jù)科學(xué)家?guī)兔涂梢詷O大地提高生產(chǎn)力。

**Automating Data Science: Democratization**