This free DevOps course provides an extensive look at Kubernetes, blending on-premises and cloud solutions with a focus on industry best practices. It is designed for both beginners and experienced developers, covering key aspects like development process optimization, application deployment, and infrastructure scaling. The course offers deep insights into Kubernetes, preparing learners for a successful tech industry career. Organized into well-structured lessons, it includes a wealth of resources and code snippets from various external repositories. This course is an invaluable opportunity for those seeking to enhance their DevOps skills and industry knowledge.
ML-based automated issue management, defect classification and bug triaging.
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.
Peter Gagarinov shares his experience with integrating Apache Ignite with external machine frameworks.
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.