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AAANE: Attention-Based Adversarial Autoencoder for Multi-scale Network Embedding Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Bridging the Gap Between Research and Production with CODE

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

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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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.

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Footnotes
3
Adding more languages will actually inflate the average accuracy because most other languages can be easily identified by looking at the character alone and have an accuracy close to 1 (e.g. Chinese, Korean).
 
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Metadata
Title
Bridging the Gap Between Research and Production with CODE
Authors
Yiping Jin
Dittaya Wanvarie
Phu T. V. Le
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16141-5

Electronic ISBN: 978-3-030-16142-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16142-2_22

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