> 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/ai-engineering/model-formats.md). # Model Formats A trained model has to leave the notebook. The page first says why pickling a Python object is the wrong way to save it, then shows PMML as a format another application can read. The default habit is pickle, and the case against it is [Stop using pickle, use JSON or PMML](https://faoel.medium.com/stop-using-pickle-to-save-machine-learning-models-eaa46e8e561a), Elfao's argument to stop using pickle to save machine learning models. ## PMML Once pickle is off the table, PMML is an XML description of a model that other applications can read. [PMML](http://www.kdnuggets.com/faq/pmml.html): an XML file that describes a machine learning model that is transferable between applications. The specification's [PMML](http://dmg.org/pmml/v4-3/GeneralStructure.html) page, PMML 4.3 - General Structure, sets out two rules. The structure of the models is described by an XML Schema, and one or more mining models can be contained in a PMML document. --- # 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/ai-engineering/model-formats.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.