ML systems that survive production

I build the infrastructure that keeps models alive: training pipelines, feature stores, eval harnesses, inference serving.

Every article published here starts from real systems, with metrics and a reproducible protocol.

Latest articles

• EmbeddingGemma 2: a test bench for replacing BGE-M3 in my RAG — Plat du jour, Oct 30, 2026
  Google released EmbeddingGemma 2 on 6 October: 271 million parameters for the text part, 768-dimension vectors you can cut down to 256, Apache 2.0 licence. On my server, BGE-M3 shares an eighth of a GPU with a reranker. The POC to compare both on my own documents, and the fp16 trap.
• A Hugging Face model inside a Docker image: four settings to make sure it is really there — To go, Oct 30, 2026
  The model is downloaded at build time, and at startup the container looks for it somewhere else or downloads it again. Four settings I now apply to every image, learned the hard way on my ml-runner.
• Mistral Large 4 goes open weight: good news, but I won't be running it — Apéro, Oct 30, 2026
  At the end of October, Mistral published the weights of Large 4, a model of roughly a trillion parameters. For Europe it is real progress. For someone serving models on an L4, the numbers add up fast, and the licence has stepped back compared with Large 3.

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