Machine Learning on Big Data
Tyson Condie #1, Paul Mineiro #2, Neoklis Polyzotis *3, Markus Weimer#4
# Cloud and Information Services Lab, Microsoft One Microsoft Way, Redmond, USA

Abstract—  

   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.

I. INTRODUCTION

II. TARGET AUDIENCE

C. Open Research Issues

Those open problems include: (1) Query and runtime optimizers for iterative, distributed programs. (2) Declarative specifications and optimizers for asynchronous computations and (3) Semantic models of non-standard fault tolerance policies.

Machine Learning on Big Data.pdf