MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
This work provides a unified tool for scalable machine learning, addressing integration challenges for developers and researchers working with big data ecosystems.
The authors introduced MMLSpark, an ecosystem that extends Apache Spark for various machine learning tasks, and presented Spark Serving for running Spark programs as low-latency web services. They demonstrated its application in a deep object detection method for Snow Leopard conservation without human-labeled data.
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation. Furthermore, we present a novel system called Spark Serving that allows users to run any Apache Spark program as a distributed, sub-millisecond latency web service backed by their existing Spark Cluster. All MMLSpark contributions have the same API to enable simple composition across frameworks and usage across batch, streaming, and RESTful web serving scenarios on static, elastic, or serverless clusters. We showcase MMLSpark by creating a method for deep object detection capable of learning without human labeled data and demonstrate its effectiveness for Snow Leopard conservation.