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Ines Mota

Nov 1

A checklist before you ship a notebook model

#machinelearning #python #mlops

1 min read

Nov 1

RTX 3090 vs A100 for a 7B model on vLLM: the cheaper GPU depends on your answer time

#vllm #llm #mlops #gpu

2 min read

kohei matsunobu

Oct 31

Before You Burn GPU Hours, Smoke-Test One Task on a Cheap CPU Runtime

#python #testing #pytest #mlops

5 min read

Oct 30

Mistral Large 4 Weights Are Out – What It Means for Building LLM Infrastructures

#ai #llm #mlops

4 min read

George Ezejiofor

Oct 29

Canary Releases for an ML Model on KServe: Replayed Traffic, Sidecar Metrics and Automatic Rollback

#kubernetes #machinelearning #mlops #kserve

17 min read

Suresh Kumar Pallapothu

Oct 29

Day 29: Managing Technical Debt, Drift and Model Governance in Enterprise IT

#ai #mlops #governance #30dayaiseries

3 min read

Ines Mota

Oct 29

The boring half of shipping a model

#machinelearning #mlops #python

1 min read

Tomasz Krystman

Oct 29

Building an MLOps Workflow: From Experiment to Production

#mlops #machinelearning #devops #tutorial

7 min read

Oct 28

Anthropic’s Claude Training Controversy: What It Means for AI Engineers

#ai #security #mlops

4 min read

Oct 28

Same RTX 3090, 22x cheaper per token: what concurrency does to vLLM cost

#vllm #llm #gpu #mlops

2 min read

Oct 27

Anthropic’s S‑1 Filing: What It Means for AI Engineers and How to Start Using Claude Today

#ai #mlops #cloud

4 min read

Francisco Booth

Oct 26

Why Your Training Script Will Fail on the Next GPU Pod Even If It Worked Last Time

#machinelearning #python #mlops #runpod

3 min read

Suresh Kumar Pallapothu

Oct 25

Day 25: Automated CI/CD — Automating the Software Lifecycle for AI Models

#ai #mlops #cicd #30dayaiseries

3 min read

Oct 25

Kimi K3.1 Pro & High‑Speed: What It Means for AI‑Heavy DevOps

#ai #mlops #cloud

4 min read

Oct 24

Kimi K3.1 Pro & Highspeed: What It Means for AI‑Powered DevOps

#ai #devops #mlops

4 min read

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