Introduction Over the past few months, I set out to answer a simple question: What does...
Introduction
Over the past few months, I set out to answer a simple question:
What does it take to build a production-style MLOps platform from scratch?
While tools like MLflow, SageMaker Model Registry, and Kubeflow provide powerful capabilities, I wanted to understand the underlying architecture and engineering decisions behind them. Instead of only using existing platforms, I decided to build my own.
The result is Kimchi, a self-hosted MLOps platform that supports:
Para 1 — The problem "Most ML tutorials end at model.fit(). Getting a model into production is...
I abandoned this Kubernetes platform on April 4th. 48 days later I rebuilt it: CrashLoopBackOff everywhere → self-service GitOps,…
Introduction At a high level, analytics platforms seem deceptively simple. A user clicks a...
Introduction: The Day Your Demo Dies Every LLM engineer has a moment like this. Your demo works...
aws.amazon.comImplement a secure MLOps platform based on Terraform and GitHub
aws.amazon.com
A tour of my self-hosted infrastructure - from reverse proxy to local AI, CI/CD pipelines, document management, and home…
