Machine Learning

Alliedium AIssistant (2020-present)

ML-based automated issue management, defect classification and bug triaging.

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.

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

Peter Gagarinov shares his experience with integrating Apache Ignite with external machine frameworks.

PyTorch Hyperlight (2020-present)

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.

PyTorch ML Development Environment (2020-present)

A set of modern ML-oriented conda and pip packages with versions carefully chosen to make sure a seamless and conflict-free integration.

Using Apache Ignite to boost the development of Jira Cloud apps

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.

Telegram Bot for Neural Style Transfer (2020-present)

Neural style transfer bot for Telegram implemented with Ray Serve, Papermill and AIOGram.

All-Russia Algorithmic Trading Conference

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.