> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/data/intro.md). # Introduction The business problem is now a question about records, and the next work is to get those records into a form a model can use. After this part the reader can walk datasets, processing, analytics, and data engineering in that order. The walk starts with the rows themselves, because a model is only as defined as the rows it is trained on: [Datasets](/data/datasets-intro.md) names feature types, finds a dataset, and judges whether the split and the label balance are fit to use. Those raw values are rarely on the scale or in the shape a learner expects, so [Data Processing](/data/processing-intro.md) scales, transforms, imputes, and annotates features and points at a pipeline that does it. Once the table is in shape, probability and dependence decide what a later score means, and [Analytics](/data/analytics-intro.md) reads distributions, dependence, and information measures so a feature is chosen for a reason. Finally the dataset has to live in a system other people can run, and [Data Engineering](/data/engineering-intro.md) places SQL, storage, quality, lineage, and ownership on one path from source to table. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/data/intro.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.