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引用次数: 0
摘要
工业界总是利用尖端的定量研究技术。从金融和保险,到营销和制造业,效率和优势已经通过测量、预测和产生见解而获得,但从未达到如此规模。以前可能只雇用一两个数据科学家的组织现在正在将工作扩展到几十个,如果不是几百个的话。以前只有少数组织可以吹嘘他们利用了机器学习和统计模型,现在很少能找到一个没有接触过的行业或参与者。组织现在面临着授权、扩展和衡量这些劳动力的挑战,以维持向预测经济的转变。在本次演讲中,我将讨论如何以及为什么构建Domino Data Lab平台。我将讨论我们在将记录系统引入数据科学时所面临的技术、组织和文化方面的挑战。
More than the Sum of its Parts: Building Domino Data Lab
Industry has always leveraged cutting edge quantitative research techniques. From finance and insurance, to marketing and manufacturing, efficiencies and advantages have been seized through measurement, prediction, and the generation of insights' but never at this scale. Organizations which previously may have employed one or two data scientists are now scaling the work to dozens if not hundreds of practitioners. Where previously only a handful of organizations could boast that they were leveraging machine learning and statistical models, now it's a rarity to find an untouched industry or player. Organizations are now faced with the challenges of empowering, scaling, and measuring this workforce to sustain the transformation to the prediction economy. In this talk, I will discuss how and why we built the Domino Data Lab platform. I will talk about the challenges we faced technologically, organizationally and culturally when bringing a system of record to data science.