Ibrahim Halil Koyuncu

Most of my work centres on data processing infrastructure: designing scalable, high-performance architectures for complex data pipelines and real-time analytics, in both on-premises and cloud environments. The architecture is only half the job — the other half is making it run reliably: Kubernetes platforms, automation, observability and cost-aware design.

On the machine-learning side I focus on MLOps and LLMOps: automating model deployment, monitoring and lifecycle management, so that models and LLM applications fit cleanly into the production systems around them instead of living beside them.

My background is in DevOps and software engineering. I build CI/CD pipelines, manage infrastructure as code, and tune cloud-native applications for availability, resilience and cost. I have also moved monolithic applications to microservices — work where understanding both the application and the platform makes the difference.

I write on Medium about the topics I work on, especially where practical, experience-based resources are missing. Most articles come with a working repository. Through this studio I bring the same hands-on approach to client work — and to our own products, starting with Canopeak.