Bridging the Gap Between Research and Production with CODE

Yiping Jin, Dittaya Wanvarie, Phu T. V. Le

Abstract: 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.