Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
A fast learning algorithm for deep belief nets
Neural Computation
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A common substrate for cluster computing
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
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Statistical Machine Learning has undergone a phase transition from a pure academic endeavor to being one of the main drivers of modern commerce and science. Even more so, recent results such as those on tera-scale learning [1] and on very large neural networks [2] suggest that scale is an important ingredient in quality modeling. This tutorial introduces current applications, techniques and systems with the aim of cross-fertilizing research between the database and machine learning communities. The tutorial covers current large scale applications of Machine Learning, their computational model and the workflow behind building those. Based on this foundation, we present the current state-of-the-art in systems support in the bulk of the tutorial. We also identify critical gaps in the state-of-the-art. This leads to the closing of the seminar, where we introduce two sets of open research questions: Better systems support for the already established use cases of Machine Learning and support for recent advances in Machine Learning research.