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

🔍 From Data to Decisions: Smarter Maintenance with Digital Twins

How do data, domain knowledge & anomaly detection come together in real-world systems? ✈️
Priyanka Schnell will share an aerospace case study on condition-based maintenance, early failure detection & digital twins in production.

💻 Live exploration in Google Colab
✨ No engineering or physics background needed
📅 https://www.meetup.com/rladies-rome/events/313125588/

#RLadiesRome #DataScience #DigitalTwins #MLOps #WomenInTech

The solution class isn't "pick the perfect scheduler."

It's make the allocation model legible:
→ Explicit baselines (quotas) so planning is possible
→ Borrowing of idle capacity so utilisation doesn't tank
→ Priority tiers with preemption contracts
→ Shared unit economics so finance and engineering argue from the same facts

Priority queues work when people believe the system is fair.
That belief is governance. The scheduler just enforces it.
#MLOps #PlatformEngineering #FinOps

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/

#gpu #nvidia #kubernetes #machinelearning #mlops #ai

Each phase optimises for something different. The Wild West optimises for local speed. Static quotas optimise for local safety. Flexible borrowing optimises for global throughput and, maybe more importantly, legitimacy. (Everyone understands the rules.)

I'll be writing more about queuing and priority over the next couple of weeks. There's a lot to unpack here.

#MLOps #MachineLearning #PlatformEngineering #GPU

If you're in one of these roles: you're doing something undeservedly difficult. The technical complexity alone is immense. Doing it while navigating organisational politics, competing priorities, and limited recognition? That takes something special.

Hats off to you. Your work matters... even when nobody says it!

#MLOps #PlatformEngineering #MachineLearning

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.

#OpenSource #MLOps #SelfHosting #ai #BuildInPublic

📰 MLOps 2026: Why Model Serving and Inference are the New Frontier

Stop guessing your inference costs. Explore the 2026 MLOps landscape, featuring deep dives into LLM optimization, Edge AI, and automated drift detection.

#mlops #ai #devops #news

🔗 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

Matches one of the ways I like to learn - not just reading docs, but actually debugging and fixing things myself. Different people learn differently. For me, poking at broken code from multiple angles makes concepts stick. (And reading the docs, and writing or blogging about things, etc etc!)

Nothing fancy - just an experiment in interactive learning.

https://github.com/strickvl/zenlings for the repo. YMMV!

#MLOps #ZenML #LearningInPublic

Thinking about jumping on the hype train 🚆 and adding OpenClaw (openclaw.ai) to the MLOX service stack… 🤔

For those who haven’t heard of it: aww… you have 😄

#OpenSource #MLOps #AI #SelfHosting

Cloud spending hit $107 billion in Q3 2025, according to Synergy Research Group data. AWS around 29%, Azure 20%, and Google Cloud 13%.

Sure, they offer exceptional tech and service, right?

Well, let's take an orchestrator like Apache Airflow (no exception): it'll easily set you back €300+/month, whereas a comparable setup on self-hosted hardware (e.g., a VPS) can be €5/month. 2x I get it, 5x maybe, 10x that's greedy-but 100x more?

Self-hosting!

#Cloud #MLOps #DevOps #SelfHosting #OpenSource

Each traverse_node is a separate step, created at runtime, with its own artifacts, retries, and lineage.

#MLOps #ZenML #agents #pipelines #LangChain

Avi Chawla (@_avichawla)

임베딩 스택이 모델을 바꿀 때마다 100% 재인덱싱을 강제한다는 문제 제기. 많은 팀이 이를 불가피하다고 여기며, 예를 들어 대형 임베딩 모델로 RAG 파이프라인을 구성해 프로덕션에 배포한 뒤 시간이 지나 트래픽·요구사항이 바뀌면 전체 재인덱싱 비용과 운영 부담이 크게 증가한다는 점을 지적하고 있음.

https://x.com/_avichawla/status/2018210296608022845

#embeddings #rag #reindexing #mlops

24 hours until the CfP for "LLMday NYC 2026 Q1" closes: https://papercall.io/cfps/6488/submissions/new

#cfp #conference #Llms #Prompt engineering #Ai #Ml #Mlops

TechFollow (@TechFollowrazzi)

Micah Hill-Smith는 ArtificialAnlys의 공동창업자 겸 CEO로, 독립적인 AI 벤치마킹 플랫폼을 운영해 팀들이 특정 사용 사례에 맞는 최적의 모델과 API 제공자를 선택하도록 도와줍니다. 모델 평가·비교에 특화된 서비스라는 점이 강조됩니다.

https://x.com/TechFollowrazzi/status/2017990678022676755

#aibenchmarking #modelevaluation #aitools #mlops

LLM-generated EHR summaries can fail in the worst way: one confident hallucination that changes clinical meaning.

This guide shows claim-level evaluation, risk-weighted safety metrics, and production gates (generate → verify, conservative fallbacks, monitoring).
Read: https://codelabsacademy.com/en/blog/evaluating-llm-hallucinations-clinical-safety-ehr-summaries?source=mastodon

#HealthcareAI #ClinicalNLP #MLOps #PatientSafety #EHR

Dự án mã nguồn mở PardusClawer hỗ trợ data scientist, tích hợp Ollama & tự động tìm kiếm dữ liệu qua web. Cộng đồng Reddit giới thiệu công cụ đáng thử từ tác giả /u/jasonhon2013.

#DataScience #OpenSource #AI #Python #MLOps
#KhoaHocDuLieu #MaNguonMo #TruongHopSVM #HocMay #RedditVietNam

https://www.reddit.com/r/LocalLLaMA/comments/1qt05j3/openclaw_for_data_scientist/

Cloud is "stable", right?

2025 reminded us that even the biggest providers can fail spectacularly: outages at AWS, Cloudflare, Google and others affected *millions* globally and cascaded into major service disruptions. Data aggregated by Ookla paint a stark picture of how reliant we are on a handful of providers.

Do you think *small companies and devs* are taken seriously by these cloud giants?

https://www.ookla.com/articles/largest-outages-of-2025

#Cloud #MLOps #DevOps #Resilience #AI #SelfHosting

Thinking about a new MLOX playground 🛠️

Recently, the new Raspberry Pi AI HAT+ 2 was announced. An accelerator add-on for the Raspi 5 that brings local generative AI capabilities to the Pi delivering ~40 TOPS of inferencing performance.

I'm tempted to order one now and see how far we can push local AI workflows on tiny hardware. Luckily, MLOX makes experimenting easy, I already tested it with raspi zero 2W which worked like a charm.

#AI #MLOps #SelfHosting #OpenSource #RaspberryPi

Next week: how ZenML approaches dynamic pipelines specifically.

#MLOps #ZenML #agents #LangChain