Notes on machine learning
Technical notes written while learning: honest evaluation, data leakage, deployment, LLM applications and the mistakes behind each lesson. They come from the projects on this site, with the code one click away.
transformers.js, ONNX Runtime Web and tfjs: I benchmarked all three browser ML runtimes with real numbers
I built a benchmark to measure transformers.js, ONNX Runtime Web and tfjs running the same sentence-embedding task, plus an identical MLP on the two runtimes that can actually run it. WASM beat WebGPU on both tasks, tfjs's backend choice mattered more than which runtime I picked, and along the way I found a backend that would not even start by default on this machine.
Read articleRunning models in the browser: what building a whole lab taught me
This portfolio has over twenty ML demos running in your browser with no server: networks training live, a transformer generating text, diffusion, a GAN. This is the practical guide that would have saved me weeks: formats, sizes, traps, and which technique fits which job.
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Post-mortem: twelve days shipping a broken deploy without knowing
From June 29 to July 11 this site did not deploy once, while I was convinced it did. The full story of Cloudflare's 3 MB limit, how I found it, how I fixed it, and the three lessons I am keeping.
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Publishing ten CLIs on PyPI: what nobody tells you
Names already taken, a new-project limit that is not in the documentation, and why the package name does not have to match the command name. Notes from publishing a full suite of tools.
Read articleFrom notebook to an inference API that survives production
A model trained in a notebook is not a product. Turning it into a service that validates its input, responds with predictable latency and can be monitored is the work that separates a demo from a system.
Read articleMLOps: the difference between a model in a notebook and one in production
A notebook is an exploration environment, not a system. What it takes for a model to work in production, maintain itself and not degrade silently.
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Drift: how to detect that your model is degrading
The model does not change. The data does. And if nobody is watching, the model can be giving bad predictions for weeks without anyone knowing. How to monitor drift in practice.
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