실리콘밸리 IT 기업에서 머신러닝은 이제 서비스를 제공하는데에 있어서 없어서는 안될 기술로 자리 잡았습니다. 그 대표적인 기업들중 하나인 LinkedIn 사례를 통해 어떤 과정을 거쳐서 머신러닝이 각 서비스에 제공되고 발전하는지 알아보겠습니다. 또한 회사들이 머신러닝을 이용한 서비스를 제공함에 있어서 어떠한 실질적인 문제들에 직면하고 있는지 소개하고 이에 대한 몇가지 해결 방안도 예제를 통해 논의하겠습니다.

Myunghwan Kim is a senior applied research scientist at SNA in LinkedIn. He received his PhD degree from Stanford University in 2014 under the supervision of Jure Leskovec. His main research topic was modeling social networks where auxiliary information such as node attributes or timestamps are given. This research required applying the advanced machine learning into graph data such as social networks. Besides the main research, he also worked on human mobility, topic modeling, and big data. He was a member of SNAP group and InfoLab. Before PhD, he also completed MS in Statistics at Stanford University. His main research intrests include social network analysis, which includes social network modeling, network evolution modeling, and network inference/completion. At LinkedIn, he is also working for the team providing PYMK (People You May Know) service. PYMK service is the connection recommendation service for +300M LinkedIn members.