There’s a stat every AI practitioner should know: ~88% of AI projects die before they ever deliver real value in production 😲

This isn't primarily a model quality issue or lack of compute-the tech works. The real killers are things like (and I quote) *“the infrastructure nightmare”*.

If you want to sleep tight without nightmares, that's exactly the space I'm trying to improve with mlox.org 🙂

The full article:
https://dev.to/ambalogun/the-88-problem-why-most-ai-projects-die-between-pilot-and-production-535m

#AI #MLOps #DevOps #Infrastructure #Production

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Как мы запускаем LLM on-prem в Kubernetes и выжимаем максимум из GPU-кластера

Всем привет! В этой статье я расскажу, как мы запускаем большие языковые модели на Kubernetes-платформе Nova AI. Разобьем материал на две части: сначала посмотрим, с помощью чего это реализовано (архитектура и компоненты), а затем — что это позволяет делать (сценарии использования и практические кейсы).

https://habr.com/ru/companies/orion_soft/articles/993488/

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Build-in-public moment:

I’m wiring up OpenClaw to be deployed on any server/VPS via MLOX-with just the press of a button.
Status: works in principle, breaks in the details 😅

Still promising enough to keep pushing. If anyone feels like picking this up or collaborating on it, I’d be very happy to hand it over.

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🔗 https://dataformathub.com/blog/mlops-2026-why-model-serving-and-inference-are-the-new-frontier-yuv?utm_source=mastodon&utm_medium=social&utm_campaign=blog