Declarative Systems for Machine Learning


Functional programming models like MapReduce raise the level of abstraction. They allow the programmer to focus on implementing her algorithm in a clean abstraction without the concern of parallelism, data distribution or fault-tolerance. Unfortunately, MapReduce runtimes like Hadoop do not provide a high-performance solution for machine learning, nor do they provide an attractive API. The ScalOps project seeks to address this shortcoming on multiple levels. Firstly, we aim to provide an efficient runtime that directly supports not only a rich Pig-like set of operators, but also iterations to facilitate many computations from the machine learning domain. Secondly, we provide a programming language that targets machine learning algorithms. This domain specific language (DSL) is written in the Scala programming language; a JVM-based language that is byte-code compatible with Java. In this talk, I will report on the current status of the ScalOps project and our runtime layer called Hyracks.

Watch the video of Markus Weimer's talk here.

Schedule info
Time slot: 
4 June 17:05 - 17:45
Experience level: 
Presentation Format: 
Long (40min)