# Introduction to Model Management

AI model development and optimization require frequent iterations and debugging. Changes in datasets, training code, or parameters may affect the quality of models. If the metadata of the development process cannot be managed in a unified manner, the optimal model may fail to be reproduced.

ModelArts model management allows you to import models generated with all training versions to manage all iterated and debugged models in a unified manner.

#### Usage Restrictions

- In an automatic learning project, after a model is deployed, the model is automatically uploaded to the model management list. However, models generated by automatic learning cannot be downloaded and can be used only for deployment and rollout.

#### Model Management Functions

Table 1 Model management functions
Supported Function | Description
--- | ---

Importing a Model

Import the trained models to ModelArts for unified management. You can import models using four methods. The following provides the operation guide for each method.

- Importing a Meta Model from a Training Job
- Importing a Meta Model from a Template
- Importing a Meta Model from a Container Image

Managing Model Versions | To facilitate source tracing and repeated model tuning, ModelArts provides the model version management function. You can manage models based on versions.

Parent topic: Model Management