Machine learning
The root page of this wiki: fitting a model to data so it performs a task no one wrote rules for. The spoke treats machine learning as an engineering discipline rather than a body of theory — what it costs to train, what it takes to adapt, and what breaks between a demo and a deployment.
The three layers this corpus covers
A model’s life runs through stages, and the founding sources land on three of them:
| Layer | What it asks | Founding source |
|---|---|---|
| Algorithm | How does a model fit data at all? | mlalgorithms → classical-ml-algorithms |
| Adaptation | How is a pretrained model turned to a new task? | finetuning-explained-kdnuggets, unsloth-amd-support → fine-tuning |
| System | What gets built around the model so it earns its keep? | ml-system-design-case-studies, ai-projects-fail-infrastructure-people → ml-system-design, demo-to-production-gap |
The gap in the middle is the founding one: nothing here yet covers training a model from data end to end — data collection, labelling, pretraining, evaluation. The corpus jumps from the fitting procedure to adapting somebody else’s finished model. See synthesis.
Underneath the three layers
information-theory-inference-learning-algorithms added a fourth thing the corpus lacked — not a stage in a model’s life but the theory beneath all of them. Fitting a model is inference over its parameters (bayesian-inference), the loss you minimize is a likelihood, model complexity is priced by the evidence rather than by a penalty you choose, and the same message-passing machinery decodes an error-correcting code (information-theory). It is the spoke’s only source that explains why any of this works, and its only T1. It is also twenty-three years old and says nothing about what training costs.
Where the rest of it lives
The hub splits a model’s life across spokes, and this one is deliberately train-time and
design-time. Serving mechanics (sampling, KV cache, batching) are ../llm-inference-wiki; the
provider and pricing market is ../llm-providers-wiki; agent frameworks are
../agentic-tooling-wiki; running the production platform is ../platform-ops-wiki. Full
boundaries in this spoke’s CLAUDE.md.
Related
classical-ml-algorithms · fine-tuning · ml-system-design · demo-to-production-gap · bayesian-inference · information-theory · monte-carlo-methods · synthesis