ML-based automated issue management, defect classification and bug triaging.
Finetuned host and Docker network isolation for Arch Linux.
A curated list of awesome software engineering resources.
Building a distributed and scalable ML-enabled backend on top of Apache Ignite, Ray Serve, Scikit-learn and PyTorch.
Local development with Apache Ignite made simpler on Arch Linux.
Peter Gagarinov shares his experience with integrating Apache Ignite with external machine frameworks.
The ML micro-framework built on top of [PyTorch-Lightning](https://pytorchlightning.ai/) and [Ray Tune](https://docs.ray.io/en/master/tune/) to push the boundaries of simplicity even further.
A set of modern ML-oriented conda and pip packages with versions carefully chosen to make sure a seamless and conflict-free integration.
Peter shares his Apache Ignite experience. He will show how one can minimize the number of blocks in a complex, scalable backend for an ML-based, automated issue-management system (Alliedium), as you stay within the Java ecosystem and the microservice paradigm.
Neural style transfer bot for Telegram implemented with Ray Serve, Papermill and AIOGram.