Medium ·
Scalable machine learning with Apache Ignite, Python and Julia: from prototype to production
Building a distributed and scalable ML-enabled backend on top of Apache Ignite, Ray Serve, Scikit-learn and PyTorch.
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.
Cite
@inproceedings{scalable_ml_ignite_python_julia,
title={Scalable machine learning with Apache Ignite, Python and Julia: from prototype to production},
author={Peter Gagarinov},
booktitle={Medium},
year={2021},
organization={Alliedium}
}