ML-based automated issue management, defect classification and bug triaging.
Building a distributed and scalable ML-enabled backend on top of Apache Ignite, Ray Serve, Scikit-learn and PyTorch.
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.
Peter shares his experience with building the ML-based semi-automatic market making and position trading system utilizing statistical arbitrage opportunities in volatility index – equity index future spreads on US market.