# Keep a versioned model repository In some cases you will want the ability to know why a decision was made, for example, if there is an unexpected output or someone challenges a recommendation. Indeed, in most regulated environments it is essential to be able to show how a given decision or recommendation was reached, so you know which version of your machine learning model was live when a specific decision was made. To meet this need you will need a store or repository of the models that you can query to find the version of the model in use at a given date and time. **In the past we have used a variety of ways to version our models:** * S3 buckets with versioning enabled * S3 buckets with database to to store model metadata * MLflow model registry * DVC to version both the model and the data used to create that model * Cloud provider model registries (AWS Sagemaker, Google Vertex AI , Azure MLOps) * Some models can have their coefficients stored as text, which is versioned in Git --- # 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://playbooks.equalexperts.com/mlops-playbook/practices/keep-a-versioned-model-repository.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.