Tag: #ml
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 2 posts
Multi-GPU, Multi-Node Training Platforms: The Complete Map — from the Framework Ecosystem to Hands-On Slurm and Kubeflow Guides
A one-page map of the full landscape of training models across multiple GPUs and multiple nodes. The AI library and framework ecosystem map (PyTorch, JAX, HuggingFace, DeepSpeed, Ray), when to pick which parallelization
2026-07-09 · 8 min read #ai#ml#distributed-training#slurm#kubeflowAI Model Development, Start to Finish — a Realistic Lifecycle from Data to Deployment
Model development starts not with pretraining but with a decision ladder — does prompting suffice, does RAG suffice, do you need fine-tuning? The eval-first principle of building your test set before your model, data qua
2026-07-07 · 8 min read #ai#ml#llm#mlops#training