Scalable machine learning with Apache Ignite, Python and Julia: from prototype to production

Abstract

In the previous article Boosting Jira Cloud app development with Apache Ignite we explained the benefits of using Apache Ignite in a combination with Spring Boot for building scalable and distributed backends for Jira add-ons. The important aspect of the backend design left without much attention was the machine learning infrastructure. In this post I’ll explain why we started with a combination of Scikit-learn and Celery and ended up with a combination of Apache Ignite, Ray Serve, Scikit-learn and PyTorch.