Inside the AI Runtime: PyTorch, C++, CUDA and Beyond
Understand the PyTorch runtime, from C++ and CUDA internals to TorchScript and AI-generated runtimes like VibeTensor, for high-performance AI.
Pytorch
Layers of AI Observability for LLMs, Agents and Secure Operations
Discover the essential layers of AI observability to make LLMs and agents reliable, secure, cost‑efficient and compliant in real‑world production.
Language Models From Scratch: From Tokens to Local LLMs
Understand large language models from tokens to transformers, quantization and local execution, explained in clear, practical English.
Microsoft Maia 200: the new in‑house AI accelerator redefining inference at scale
Maia 200 is Microsoft’s new in-house AI chip for fast, efficient inference in Azure, rivaling Trainium and TPU with over 10 PFLOPS of FP4 power.
Implementing Reinforcement Learning: From Theory to Real‑World Systems
Learn how reinforcement learning works, its algorithms, uses, risks and how to implement RL in real projects step by step.
AI model collapse in generative design and knowledge tools
Discover how AI model collapse affects generative design tools, why synthetic data is risky and what strategies can prevent long‑term degradation.
Artificial Intelligence with Python: Libraries, Uses and Tools
Discover how to use Python for AI: libraries, examples, tools and real applications explained clearly and in depth.
AI Hallucinations: Why Smart Models Still Make Things Up
Discover why AI hallucinations happen, real examples, their risks and the best current techniques to detect and reduce them.
Open‑Source Language Model Evaluation Platforms Explained
Discover the key open‑source and enterprise platforms to evaluate, monitor and govern modern language models and LLM agents.