Machine Learning

A modality-aware machine learning project template that gets you from idea to training in minutes using uv, Polars, and LitServe for modern ML workflows.

Fix your ML training workflow with the Pre-Flight, In-Flight, Post-Flight framework—catch bugs early, monitor runs, and evaluate beyond vanity metrics.

Scale ML models faster with the right training infrastructure. Covers distributed training, GPU clouds, spot instances, and cloud-agnostic setups.

How the histories of full stack web development and machine learning converged to create the full stack machine learning engineer role.

The ML ecosystem mirrors JavaScript's 2015 framework chaos. I map the fragmentation layers and share how I tackled it by building torchwebio.