  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.
