# Models & Versions ### Model In Aporia, a `model` is any system that can make predictions and can be improved through the use of data. We use this broad definition in order to support a large number of use cases. Some examples of a model include: * a simple Pytorch model * an ensemble of 15 XGBoost models, 37 LightGBM models, and a few deterministic algorithms * or even an evolutionary algorithm Aporia models usually serve specific business use cases: Fraud Detection, Credit Risk, Patient Diagnosis, Churn Prediction, LTV, etc. ### Model Version Each `model` in Aporia can have different `version`. When you (re)train your `model` or update a model's `schema` you should create a new model version in Aporia (via the **Versions page** or **SDK**). When creating a new model version in Aporia, you'll be able to specify the model version's `schema` - a definition of the inputs and outputs of the model. --- # 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.aporia.com/core-concepts/model-versions.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.