Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning
Ignacio Arnaldo, Kalyan K. Veeramachaneni, Andrew H. Song, Una-May O’Reilly · IEEE Computational Intelligence Magazine · 2015
We introduce FCUBE, a cloud-based framework that enables machine learning researchers to contribute their learners to its community-shared repository. FCUBE exploits data parallelism in lieu of algorithmic parallelization to allow its users to efficiently tackle large data problems automatically. It passes random subsets of data generated via resampling to multiple learners that it executes simultaneously and then it combines their model predictions with a simple fusion technique. It is an example of what we have named a Bring Your Own Learner model. It allows multiple machine learning researchers to contribute algorithms in a plug-and-play style. We contend that the Bring Your Own Learner model signals a design shift in cloud-based machine learning infrastructure because it is capable of executing anyone's supervised machine learning algorithm. We demonstrate FCUBE executing five different learners contributed by three different machine learning groups on a 100 node deployment on Amazon EC2. They collectively solve a publicly available classification problem trained with 11 million exemplars from the Higgs dataset.