# ML Models ![Model UI](/files/C0vrIF2pSOScfcuMGXuW) Model definitions allow reuse of data science libraries and model classes, running algorithms with different set of inputs and parameters. Initial definition of a model includes: * **Name:** A descriptive name * **Description:** Detailed description of the model * **Tags:** Descriptive tags for the model * **Status:** Whether this model should be deployed or not * **Version:** Current version of the model, which is used for deciding whether real-time model handlers need to update their current assets or not * **Domain:** Categorization of the model based on business domain * **Assets Root:** Root path in file system for storing and retrieving saved model assets * **Assets Directory:** Directory under root for saved model assets * **Scheduler:** Scheduler to use for regular training or execution of the model (e.g. Airflow) * **Schedule Status:** Whether schedule is currently active or paused * **Comments:** Historical list of comments, which typically includes information on model update reasons or findings * **Parameters:** Model level parameters which can be for training or inference purposes Each model consists of a series of steps, which allows building sequential model pipelines, although most models may consist of a single step. Initial definition of a step includes: * **Id:** Unique identifier for the step * **Name:** A descriptive name * **Description:** Detailed description of the step * **Has Assets:** Whether step has stored assets or not * **Assets Root:** Root path override for the model assets root * **Assets Identifier:** File name for the step assets Steps also have parameters that are defined in a very dynamic manner to allow configuration of all model types, and may include input, output, training details as well as the class and package names for Python libraries. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.rierino.com/data-science/ml-models.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.