Deploying Machine Learning (ML) algorithms within databases is a challenge. This article introduces an Apache Spark-based micro-service orchestration framework. This allows users to integrate ready-to-use intelligence into any datastore with an Spark connector.
Deploying Machine Learning (ML) algorithms within databases is a challenge
due to the varied computational footprints of modern ML algorithms and the
myriad of database technologies each with its own restrictive syntax. We
introduce an Apache Spark-based micro-service orchestration framework that
extends database operations to include web service primitives. Our system can
orchestrate web services across hundreds of machines and takes full advantage
of cluster, thread, and asynchronous parallelism. Using this framework, we
provide large scale clients for intelligent services such as speech, vision,
search, anomaly detection, and text analysis. This allows users to integrate
ready-to-use intelligence into any datastore with an Apache Spark connector. To
eliminate the majority of overhead from network communication, we also
introduce a low-latency containerized version of our architecture. Finally, we
demonstrate that the services we investigate are competitive on a variety of
benchmarks, and present two applications of this framework to create
intelligent search engines, and real-time auto race analytics systems.