This talk discusses the trajectory of MLlib, the Machine Learning (ML) library for Apache Spark. We will review the history of the project, including major trends and efforts leading up to today. These discussions will provide perspective as we delve into ongoing and future efforts within the community. This talk is geared towards both practitioners and developers and will provide a deeper understanding of priorities, directions and plans for MLlib.
Since the original MLlib project was merged into Apache Spark, some of the most significant efforts have been in expanding algorithmic coverage, adding multiple language APIs, supporting ML Pipelines, improving DataFrame integration, and providing model persistence. At an even higher level, the project has evolved from building a standard ML library to supporting complex workflows and production requirements.
This momentum continues. We will discuss some of the major ongoing and future efforts in Apache Spark based on discussions, planning and development amongst the MLlib community. We (the community) aim to provide pluggable and extensible APIs usable by both practitioners and ML library developers. To take advantage of Projects Tungsten and Catalyst, we are exploring DataFrame-based implementations of ML algorithms for better scaling and performance. Finally, we are making continuous improvements to core algorithms in performance, functionality, and robustness. We will augment this discussion with statistics from project activity.
Session hashtag: #SFdev1
Joseph Bradley is a Spark Committer working on MLlib at Databricks. Previously, he was a postdoc at UC Berkeley after receiving his Ph.D. in Machine Learning from Carnegie Mellon U. in 2013. His research included probabilistic graphical models, parallel sparse regression, and aggregation mechanisms for peer grading in MOOCs.