Bridging the Gap Between Research and Production with CODE
Despite the ever-increasing enthusiasm from the industry,artificial intelligence or machine learning is a much-hyped area where the results tend to be exaggerated or misunderstood.Many novel models proposed in research papers never end up being deployed to production.The goal of this paper is to highlight four important aspects which are often neglected in real-world machine learning projects,namely Communication,Objectives,Deliverables,Evaluations(CODE).By carefully considering these aspects,we can avoid common pitfalls and carry out a smoother technology transfer to real-world applications.We draw from a priori experiences and mistakes while building a real-world online advertising platform powered by machine learning technology,aiming to provide general guidelines for translating ML research results to successful industry projects.
Machine learning Project management Online advertising Real-time bidding
Yiping Jin Dittaya Wanvarie Phu T.V.Le
Department of Mathematics and Computer Science,Chulalongkorn University,Bangkok 10300,Thailand Knorex Pte.Ltd.,8 Cross St,Singapore 048424,Singapore
国际会议
澳门
英文
277-288
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）