About

I'm an MLOps & Platform Engineer specialized in bringing AI out of Jupyter notebooks and into robust production environments. I build the infrastructure that makes machine learning reliable, scalable, and production-ready for the real world. Coming from a Full-Stack AI background, I have a deep understanding of the entire product lifecycle - from the backend data pipelines to the user interface. I'm also passionate about open-source software and self-hosting.

This site is where I share technical write-ups, project notes, and things I find worth documenting.

My Projects

Things I've built or actively contribute to.

docker-pipeline

Lightweight declarative orchestrator to run Docker steps — YAML pipelines, container-first runner, retries, hooks and a small HTTP API.

Stack & Tools

Tools and platforms I use regularly.

MLflow

End-to-end ML lifecycle platform — experiment tracking, model registry, prompt management.

BentoML

Framework for building and deploying ML model serving APIs at scale.

Proxmox

Open-source hypervisor for self-hosted virtualisation — runs my entire homelab infrastructure.

Ray

Distributed computing framework for scaling ML workloads — Ray Certified Developer.

Blog

Latest 10 articles: