Alliedium AIssistant (2020-2022) Last updated on Jun 26, 2024 Code Video Follow Project Website ML-based automated issue management, defect classification and bug triaging. Atlassian Jira Machine Learning Deep Learning Project Management Issue Processing Java Spring Boot Python Celery Ray Serve Kubernetes Docker Atlassian Connect Spring Boot JavaScript React Py4J RabbitMQ Apache Ignite PostgreSQL Amazon EKS Jenkins SQL Related Scalable machine learning with Apache Ignite, Python, and Julia: from prototype to production Using Apache Ignite to boost the development of Jira Cloud apps Awesome Software Engineering (2020-present) Equity Index Volatility and Correlation Trading System (2013–2015) Scalable machine learning with Apache Ignite, Python and Julia: from prototype to production Publications 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. Peter Gagarinov Cite Project Source Document Boosting Jira Cloud app development with Apache Ignite Making development of distributed and scalable Jira Cloud app backends easier with Apache Ignite and Atlassian Connect Spring Boot. Peter Gagarinov, Ilya Rublev Cite Project Source Document Events 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. May 25, 2021 Virtual Event Peter Gagarinov Project Slides 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. Mar 2, 2021 Virtual Event Peter Gagarinov Project Slides Video
