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Large Scale Machine Learning Systems

Curated by: Eric P. Xing and Qirong Ho


The rise of Big Data requires complex Machine Learning models with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In turn, this has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters. In order to support the computational needs of ML algorithms at such scales, an ML system often needs to operate on distributed clusters with 10s to 1000s of machines; however, implementing algorithms and writing systems softwares for such distributed clusters demands significant design and engineering effort. A recent and increasingly popular trend toward industrial-scale machine learning is to explore new principles and strategies for either highly specialized monolithic designs for large-scale vertical applications such as various distributed topic models or regression models, or flexible and easily programmable general purpose distributed ML platforms—- such as GraphLab based on vertex programming, and Petuum using parameter server. It has been recognized that, in addition to familiarity of distributed system architectures and programing, large scale ML systems can benefit greatly from ML-rooted statistical and algorithmic insights, which can lead to principles and strategies unique to distributed machine learning programs. These principles and strategies shed lights to the following key questions—- How to distribute an ML program over a cluster? How to bridge ML computation with inter-machine communication? How to perform such communication? What should be communicated between machines?—- and they span a broad continuum from application, to engineering, and to theoretical research and development of Big ML systems and architectures. The ultimate goal of large scale ML systems research is to understand how these principles and strategies can be made efficient, generally-applicable, and easy to program and deploy, while not forgetting that they should be supported with scientifically-validated correctness and scaling guarantees.


Related KDD2016 Papers

Title & Authors
Fast Component Pursuit for Large-Scale Inverse Covariance Estimation
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