Deep Learning with Apache SystemML
This work addresses the problem of efficient model development for enterprises using large-scale data lakes, but it appears incremental as it builds on existing big data frameworks.
The paper tackles the challenge of developing machine and deep learning models in shared big data environments like Hadoop and Spark, and presents Apache SystemML as a unified framework that automatically generates optimized runtime execution plans based on data and cluster characteristics.
Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on shared, large clusters to (3) perform many different analytics tasks ranging from model preparation, building, evaluation, and tuning for both machine learning and deep learning. Developing machine/deep learning models on data in such shared environments is challenging. Apache SystemML provides a unified framework for implementing machine learning and deep learning algorithms in a variety of shared deployment scenarios. SystemML's novel compilation approach automatically generates runtime execution plans for machine/deep learning algorithms that are composed of single-node and distributed runtime operations depending on data and cluster characteristics such as data size, data sparsity, cluster size, and memory configurations, while still exploiting the capabilities of the underlying big data frameworks.