MLOps Spanning Whole Machine Learning Life Cycle: A Survey

no code implementations • 13 Apr 2023 • Fang Zhengxin, Yuan Yi, Zhang Jingyu, LIU Yue, Mu Yuechen, Lu Qinghua, Xu Xiwei, Wang Jeff, Wang Chen, Zhang Shuai, Chen Shiping

We hope that this paper can serve as a quick reference manual (a survey of surveys) for newcomers (e. g., researchers, practitioners) of ML to get an overview of the MLOps process, as well as a good understanding of the key technologies used in each step of the ML process, and know where to find more details.

Hybrid Collaborative Recommendation via Semi-AutoEncoder

1 code implementation • 16 Aug 2017 • Zhang Shuai, Yao Lina, Xu Xiwei, Wang Sen, Zhu Liming

In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder.

Collaborative Filtering

AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders

no code implementations • 13 Jun 2017 • Zhang Shuai, Yao Lina, Xu Xiwei

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services.

Collaborative Filtering Computational Efficiency +1