ML Infrastructure
The HuggingFace Training Stack
A visual map of the full HuggingFace ecosystem - Transformers, Datasets, PEFT, Accelerate, and TRL - showing how each library fits together in a real fine-tuning pipeline.
Read guide →Visual, interactive deep-dives into ML concepts - built to make complex ideas stick. Explore guides on fine-tuning, training ecosystems, memory bottlenecks, and agent workflows.
A visual map of the full HuggingFace ecosystem - Transformers, Datasets, PEFT, Accelerate, and TRL - showing how each library fits together in a real fine-tuning pipeline.
Read guide →Why language models need GPUs, where memory goes during training and inference, and exactly what happens when it runs out - with an interactive OOM simulator.
Read guide →From full fine-tuning to LoRA and QLoRA - a comprehensive visual guide covering when to fine-tune, how to choose a strategy, and the key hyperparameters that matter most.
Read guide →Anthropic's "Building Effective Agents" taxonomy, visually: the augmented LLM, four workflow patterns, and the ReAct agent loop - each with a diagram and runnable Python code.
Read guide →A detailed teardown of seven diverse agentic systems-from Oracle's In-DB data agents to DeepAnalyze's completely autonomous LLM loop. Featuring architectures, pros/cons, and mermaid workflows.
Read guide →Before understanding any ML algorithm - from linear regression to large language models - build the mathematical intuition from first principles.
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