MLflow, W&B, and Neptune all track experiments but optimize for different teams and workflows. A practical comparison across UI quality, self-hosting, hyperparameter optimization, and pricing — with a clear decision framework.
CUDA out of memory errors are almost always solvable without buying more hardware. A practical checklist of GPU memory reduction techniques — gradient checkpointing, 8-bit Adam, LoRA, Flash Attention, and more — in the order to try them.
LoRA, QLoRA, and full fine-tuning each solve the adaptation problem differently. A practical breakdown of memory costs, quality trade-offs, and when each approach is actually the right choice for your hardware and task.
DDP, FSDP, and DeepSpeed ZeRO all distribute training across GPUs but solve different problems. A practical breakdown of when to use each, with memory calculations and a clear decision framework for 7B to 70B model training.
torch.compile delivers 20–50% inference speedup with a single line of code. A practical guide to compilation modes, handling dynamic shapes, combining with Flash Attention and quantization, and knowing when not to use it.
A practical comparison of vLLM, HuggingFace TGI, and NVIDIA Triton Inference Server for production LLM deployment — covering throughput, latency, quantization support, multi-GPU serving, and when to use each.
Visual Studio Code has become the go-to code editor for machine learning engineers worldwide, and for good reason. Its lightweight architecture, extensive customization options, and rich ecosystem of extensions make it an ideal environment for developing, testing, and deploying machine learning models. While VS Code is powerful out of the box, the right extensions can … Read more
Machine learning code fails in ways that are uniquely frustrating. A model trains for six hours and silently produces garbage predictions. A tensor shape mismatch throws an error on line 247 of a training loop. A data pipeline leaks memory so slowly you don’t notice until your cloud bill arrives. These aren’t the kinds of … Read more
Large language models like GPT-4 and Claude are impressive, but they don’t know anything about your company’s internal documents, your personal notes, or your proprietary data. Building a private AI assistant that can actually answer questions based on your specific information requires combining a local LLM with retrieval-augmented generation (RAG). This guide walks you through … Read more
Running large language models locally has evolved beyond simple inference tools into sophisticated platforms optimized for different workloads. Three solutions dominate the landscape: Ollama for simplicity and developer integration, vLLM for production-grade serving at scale, and Text Generation WebUI (oobabooga) for maximum control and experimentation. Each targets fundamentally different use cases, and choosing the wrong … Read more
