Apache Spark: Distributed Machine Learning using MLbase - Sigmoid Analytics

Apache Spark: Distributed Machine Learning using MLbase - Sigmoid Analytics

Apache Spark: Distributed Machine Learning using MLbase Implementing and consuming Machine Learning techniques at scale are difficult tasks for ML Developers and End Users. MLbase (www.mlbase.org) is an open-source platform under active development addressing the issues of both groups. MLbase consists of three components—MLlib, MLI and ML Optimizer. MLlib is a low-level distributed ML library written against the Spark, MLI is an API / platform for feature extraction and algorithm development that introduces high-level ML programming abstractions, and ML Optimizer is a layer aiming to simplify ML problems for End Users by automating the tasks of feature and model selection. In this talk we will describe the high-level functionality of each of these layers, and demonstrate its scalability and ease-of-use via real-world examples involving classification, regression, clustering and collaborative filtering. In the course of applying machine-learning against large data sets,

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