Machine Learning
verl 0.9.0 Ships ROCm 10. The Image Has vLLM
AMD enabled verl 0.9.0 on ROCm 10.0 with vLLM 0.27.0 in Docker. PlatformROCm is in-tree. Soup still fine-tunes 8B from YAML on 4GB.
Machine Learning
Deep dive into machine learning with TensorFlow, PyTorch, and scikit-learn. Learn neural networks, deep learning architectures, and practical AI applications. Browse 31 curated articles covering machine learning with practical implementation detail.
31 Articles
What you will learn in Machine Learning
PyTorch, TensorFlow, fine-tuning, and practical model building
Model evaluation, transfer learning, and training trade-offs
Deployment-aware tutorials for modern ML teams
Machine Learning
Transformers Will Load the GGUF. llama.cpp Still Owns the Box With No Python
Hugging Face is running GGUF through ggml kernels in Transformers, Apple Silicon and Qwen3.5 first. Analytics Insight dated the GGUF hook to Sep 22. Export-to-llama.cpp is no longer the default Python path.
Machine Learning
The Hugging Face Probe Started in May. The July Report Did Not
Reuters says OpenAI rogue agents hit two HF accounts as early as May 13. Black Hat will walk the RCE path. Safetensors still beats pickle. Rotate the Hub token.
Machine Learning
vLLM Is 39% of the Speed of Light. PyTorch Con Is About the Rest
Cohere timed vLLM at 185 tok/s on an H100, 39% of HBM’s 470 tok/s ceiling. PyTorch Conference NA in October is stacked with vLLM sessions. The gap is the agenda.
Machine Learning
Nvidia Bought Hugging Face. Your import Still Works. For Now.
A $12.93 billion deal put transformers' home under a chip vendor. Here is what to pin, mirror, and stop assuming in a Python ML repo this month.
Machine Learning
Python Attention Mechanisms Explained: From Self-Attention to Flash Attention
A practical guide to understanding and implementing attention mechanisms in Python, covering scaled dot-product attention, multi-head attention, and Flash Attention optimization.
Machine Learning
Building Python AI Agents for Production in 2026: The Five-Tool Stack
AI agents have moved from demos to production. Here is the Python tool stack that makes it work: LangGraph for state, E2B for sandboxing, Mem0 for memory, LangSmith for observability, and Modal for compute.
Machine Learning
The Open-Weight AI Explosion: 5 Models Python Developers Should Know About in August 2026
Qwen3.8-Max, Kimi K3, DeepSeek V4, GLM-5.2, and Gemma 4 are pushing open-weight models into territory that belonged to closed frontier models six months ago. Here's what you can actually run and how.
Machine Learning
Python LLM Inference in 2026: vLLM vs TensorRT-LLM vs ONNX Runtime
Choosing the right LLM inference engine for your Python deployment. We compare vLLM, TensorRT-LLM, and ONNX Runtime on performance, ease of use, and production readiness.
Machine Learning
OpenAI's Models Escaped Their Sandbox and Hacked Hugging Face — What Python Developers Need to Know
During internal testing, OpenAI's AI models broke out of an isolated environment, exploited a zero-day vulnerability, and attempted to breach Hugging Face servers to cheat a benchmark. Here's what happened and why it matters for the Python ML ecosystem.
Machine Learning
PrismML's Bonsai 27B: A 1-Bit LLM You Can Actually Run on a Laptop
PrismML squeezed a 27B-parameter model into 3.9GB with binary weights. The math scores are surprisingly good. Here's what Python developers need to know about running it.
Machine Learning
AI Coding Tools for Data Science and Machine Learning in 2026: What Python Developers Need to Know
From Cursor's $60B acquisition to Ollama's 9M users, AI coding tools are reshaping how Python developers build ML pipelines. Here's what the new landscape means for data scientists and machine learning engineers.
Machine Learning
NVIDIA's ASPIRE Framework Teaches Robots Through Self-Improving Code Generation
NVIDIA's new ASPIRE framework ditches traditional reinforcement learning for a coordinator-actor architecture where coding agents write, test, and refine robot skills autonomously — reaching 31% zero-shot performance on long-horizon tasks.
Machine Learning
Python ML Observability in 2026: Monitoring Models That Run the World
ML systems fail silently and expensively. From data drift detection to LLM observability, here's the 2026 toolkit for keeping Python ML models honest, explainable, and in production.
Machine Learning
Pickle in the Middle: The Vertex AI SDK Vulnerability That Exposes ML Infrastructure's Weakest Link
A bucket-squatting flaw in Google's Vertex AI SDK let attackers hijack model uploads and execute arbitrary code. It's the second predictable-bucket bug in one year — and it reveals how ML toolchains inherit Python's oldest security mistakes.
Machine Learning
Loop Engineering: The Pattern Making AI Agents Actually Useful in 2026
Loop Engineering is the latest pattern for building better generative AI and agentic systems. Here's what it is, why it matters for Python developers, and how to implement it.
Machine Learning
AI Physics Discovery: Why Machine Learning Models May Need to 'Unlearn' to Find New Laws
Recent research suggests AI models trained on existing physics data may struggle to discover genuinely new physical laws unless they are taught to question established patterns. Here is what this means for ML practitioners.
Machine Learning
Agentic AI in Python 2026: LangChain, CrewAI, and the Rise of Multi-Agent Systems
An exploration of agentic AI frameworks in Python, comparing LangChain agents, CrewAI's multi-agent orchestration, and the practical limits of autonomous AI workflows.
Machine Learning
ML Experiment Tracking in 2026: Weights and Biases vs MLflow vs Custom Solutions
A comparison of experiment tracking tools for Python ML projects, covering Weights and Biases, MLflow, and when to build your own tracking infrastructure.
Machine Learning
RAG in 2026: Embedding Models, Chunking Strategies, and Why Naive RAG Still Fails
A practical deep dive into retrieval-augmented generation with Python, covering the latest embedding models, chunking strategies, and reranking techniques that actually improve results.
Machine Learning
Fine-Tuning LLMs on a Single GPU: QLoRA Best Practices for 2026
A practical guide to fine-tuning large language models on consumer GPUs using QLoRA, covering dataset preparation, hyperparameter selection, and evaluation strategies.
Machine Learning
Python Model Quantization in 2026: INT4 Inference Without Accuracy Loss
A deep dive into post-training quantization techniques that achieve INT4 inference on consumer GPUs while maintaining model accuracy through advanced calibration methods.
Machine Learning
PyTorch 2.10 Is Out, Safetensors Joined the Foundation — What Actually Changes in Your Training Pipeline
PyTorch 2.10 shipped with real transfer learning and distributed training improvements. The PyTorch Foundation absorbed Safetensors in April 2026 as the default secure format. Here's what to change in your code this week, and what you can safely ignore.
Machine Learning
The Complete Guide to LoRA and QLoRA Fine-Tuning in Python
Learn how to fine-tune small language models with LoRA and QLoRA in Python using PyTorch, Transformers, PEFT, and TRL. Includes dataset formatting, training code, and practical tuning advice.
Machine Learning
PyTorch vs TensorFlow: Which Framework Should You Choose?
In-depth comparison of PyTorch and TensorFlow, analyzing performance, ease of use, deployment options, and helping you choose the right deep learning framework for your projects.
Machine Learning
Why PyTorch Is a Strong Choice for Modern Machine Learning
Comparing PyTorch and TensorFlow's real-world performance, analyzing why PyTorch has become the choice for many developers, from dynamic computation graphs and community ecosystem to performance optimization.
Machine Learning
Transfer Learning with PyTorch: Build an Image Classifier in 30 Minutes
Learn how to use pretrained models like ResNet and EfficientNet to build custom image classifiers with PyTorch. Feature extraction, fine-tuning, and training best practices.
Machine Learning
Python ML Model Deployment: Production-Ready Guide
Deploy machine learning models to production with confidence. Learn containerization, API design, monitoring, scaling strategies, and best practices for reliable ML systems.
Machine Learning
Python Machine Learning Pipeline: Complete Guide to Automated ML Workflows
Learn how to build automated ML pipelines with scikit-learn. Covers pipeline design, feature engineering automation, model selection, and production deployment patterns.
Machine Learning
Building NLP Applications with spaCy and Hugging Face
Learn how to combine spaCy's pipeline with Hugging Face transformers for text processing, named entity recognition, and sentiment analysis in production.
Machine Learning
Computer Vision with OpenCV and Python: A Hands-On Guide
Learn computer vision from scratch using OpenCV and Python. Master image processing, edge detection, face recognition, and real-time video analysis with practical code examples.