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Francisco Booth

Nov 7

I Ran Eight User Research Sessions With ML Engineers About GPU Training Failures. Here Is What They Had In Common.

#machinelearning #python #mlops #gpu

3 min read

Nov 7

The Silent Regression: Why AI in Production Needs Observability, Not Just Monitoring

#ai #mlops #aiengineering #observability

7 min read

김민식/학생

Nov 6

La misma molécula, otra GPU, otro puesto: cómo una tarjeta barata cambió un ranking sin dar ningún error

#ai #python #mlops #benchmarks

9 min read

Foundry 5

Nov 7

Notebook Model, Vendor Build or In-House Team: Three Ways to Ship ML

#machinelearning #mlops #uktech #softwaredelivery

3 min read

Francisco Booth

Nov 6

Your GGUF Export Will Fail At The End. The Disk Was Never Going To Be Enough.

#machinelearning #python #mlops #runpod

3 min read

SotaTek | AI & Blockchain Innovation Partner

Nov 6

#Your Model Is Not the System: Building an MLOps Feedback Loop for Production AI

#ai #machinelearning #mlops #llm

9 min read

Nov 5

After go-live: what to monitor so quality doesn't silently decay

#ai #monitoring #mlops #automation

3 min read

Muhammad Umair Ashraf

Nov 5

Build a Fine-Tuned Model Training and Serving Pipeline on AWS

#aws #machinelearning #mlops #cloud

9 min read

Viswaretas Kotra

Nov 5

How do you train on AWS spot instances without losing work?

#aws #machinelearning #mlops #gpu

9 min read

Nov 4

Catching Input Drift Before Accuracy Falls: A PSI Walkthrough

#monitoring #mlops #python #statistics

5 min read

Nov 4

Surviving the Model Treadmill: Building AI That Outlives the Model It Launched On

#ai #aiengineering #mlops #aistrategy

7 min read

Nov 4

Why the ‘Astra Can Read and Write Weights to Improve Qwen’ Claim Is Misleading – A Practical Take for Engineers

#ai #mlops #opensource

4 min read

Nov 3

Astra’s Weight‑Space Programming: A Practical Guide to Tuning Qwen

#ai #mlops #devops

4 min read

Anish Rane

Oct 4

Choosing the Right Base Model for Fine-Tuning LLMs: Strategy Before Execution

#llm #ai #machinelearning #mlops

5 min read

Nov 2

Memdoor: How a Team of 5 Qwen‑3.8‑27B Models Beats Fable‑5 on LiveCodeBench Hard

#ai #llm #mlops

4 min read

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