You can edit the code directly from the Code Editor to automatically update the model schema. Select **Update Schema** to save your changes.
### Add a Model
You can add models to AI Models using Domo's internal tools or third-party sources.
The model import modal displays.
3. Complete the following steps outlined in the modal. After completing a step, select **Next Step** to move forward.
At any time, you can select **Back** to return to the previous step or **Cancel** to exit the modal.
1. **Select an Adapter —** The adapter is the third-party source of the model.
2. **Select an Account —** You or your company must have an account with the third-party source of the model that is integrated with Domo.
* If you haven't already integrated an existing third-party source with Domo, select **Create Account** and complete account integration. Learn how to [integrate an account.](https://domo-support.domo.com/s/article/360042926054?language=en_US)
After integrating your account, restart the [Add a Model](#add_a_model) process.
3. **Import a Model —** Choose the model to import from the list of available models from that source.
4. **Configure Tasks —** Specify the following:
* Select the model task from the **Task** dropdown, which lists general model capabilities. The model account determines the available tasks. Possible task types include Binary Classification, Chat Completion, Classification, Image Generation, Regression, Summarization, Task Planning, Text Embedding, Text Generation, and Others.
* Select input or output from the **Schema** dropdown.
* Choose either `CSV` or `JSON` from the **Media Type** dropdown.
In the image below, the task type is Chat Completion, the schema is `input`, and the media type is `JSON`
This section of the modal includes a preview of the model metrics and hyperparameters, which are brought in from the selected adapter account.
* (Optional) Select **+ Add New** to add a new input/output value row to your schema.
You can edit the code directly from the Code Editor to automatically update the model schema. Select **Update Schema** to save your changes.
### Review Model Information
On the **AI Models** page, you can view the following about each model you have access to:
* The model name, description, owner, and the date it was created.
* The number of projects the model is added to.
* The current status of the model endpoint. See
[Work with Model Endpoints](#work_with_model_endpoints) to learn more.
* Metrics associated with the model. You can see more about the metrics in the
[Details view](#details_view).
The Details view includes four sections, described in the table below. The values in each section depend on the model source and how it was trained and brought into Domo.
Section | Description | Image |
|---|---|---|
Metrics | The values displayed here for metric name, value, and standard deviation are created during the initial training of the model when it is brought in from a third-party source. The model values and other results are initially compared to the sample data used in the model training. The Standard Deviation column displays even when your model doesn't have them as an output. |
|
Hyperparameters | These values are created during the initial model training and typically include items like limits, top values, variants, and any model configuration done external to Domo. |
|
Model Information | These values include the machine learning algorithm the model was trained on, the model ID used to run the model endpoint in other areas in Domo, snapshot data if a snapshot exists, and a name and link to the model job (if the model was created in Domo). |
|
Model Schema | These values include the model's assigned task, the input type, input schema, output type, and output schema. Select Edit Schema for both the input and output to update them in the schema. Learn how to edit the schema. |  |
In the modal you can [view properties](#view_schema_properties) of the schema and make the following edits: [edit](#edit_schema) rows, [delete](#delete_input_output) rows, or [add](#add_new_input_output) a new row. Select **Update Schema** to save your changes.
#### View Schema Properties
For any row in the schema, select 
#### Edit Schema
For any row in the schema, select 
#### Add New Input/Output
In the **Edit Schema** modal, selecting **+ Add New** allows you to add a new input/output value row to your schema and edit its properties.
#### Delete Input/Output
For any row in the schema, select 
# Start and Stop Endpoint
Starting and stopping the endpoint from the **Endpoint** tab also starts and stops it in other places it's being used in Domo, like Magic ETL. The endpoint status displays next to the **St** **art/Stop Endpoint** control.
# Test Endpoint
The **Test Endpoint** section of the **Endpoint** tab allows you to enter input and output text and run a test on your model. You can also copy the test output, view the schema, and view the model code snippet for the endpoint test.
Follow these steps to edit the code snippet used for the model:
1. Select **Code Snippet** to open a modal that displays the model code snippet.
2. Select the code language from the dropdown.
3. Select **Copy Code** to copy the code snippet to your machine.
4. To update the code, make changes by directly modifying the code box.
# View Endpoint Logs
The **Endpoint Logs** tab is only visible if you have started or are currently running model endpoints.
In the tab, you can see event info for any model endpoints that have run or are running.
Possible endpoint states include: Started, Running, Stopped, Failed, Active, and Snapshot Failed. The current endpoint status displays on the **AI Models** page.
### Share Access to a Model
From the **AI Models** page, select 
### Deploy a Model to Magic ETL
### Delete a Model
You can delete models you have access to from the **AI Models** page by selecting 
4. Use the dropdown to select a DataSet to create your project on and select **Go To DataSet**.
The **AutoML** tab of the DataSet opens.
5. Select **Get Started.**
6. Select the column from your DataSet you want to predict and the task type. There are four types of tasks:
* **Automatic (Recommended) —** This type uses the best task based on your data.
* **Regression —** This type measures numeric values such as revenue.
* **Binary Classification —** This type classifies boolean data such as customer churn.
* **Multi-class Classification —** This type classifies data groups such as industry, tech, and finance.
7. Select **Start Training**. A timer displays while AutoML trains and tests learning models using your data. Learn more about the [stages of training](/s/article/360048127854#training_stages).
When AutoML is finished, a **Model Overview** page displays information about the performance of the models that AutoML built.
Your AutoML project now displays on the **AI Projects** page. Learn more about [preparing data for AutoML](https://domo-support.domo.com/s/article/360048127854?language=en_US#prepare_data_for_automl) and [training and deploying models with AutoML](/s/article/360048127854).
# Create a Custom Project
Follow the steps below to create a new custom project:
1. [Access **AI Projects**](#access_ai_models_and_projects).
2. Select **New Project** to open the **Create new project** modal.
3. In the modal, select **Custom Project.**
4. Name your project and enter an optional description.
5. Select **+ Add Existing Models** to add a model to the project.
6. Select **Create New Project** to add the project to the **AI Projects** page.
Learn more about [managing AI models](/s/article/000005502).
### Add a Model to AI Projects
From the **AI Models** page, you can add a model to one or more projects by selecting 
### Compare Models
When you select a project from the **AI Projects** list, you open the project's Details view. Here, you can compare up to 10 models and 10 metrics total. The available metrics depend on the models brought into your project and how they were initially trained.
A project's Details view contains the **Metric Comparison** section, where you can view the metrics you are comparing, and the **model list**, where you can see b asic information about each model in the project. Select a model to see its additional [details](#details_view).
# Choose Models to Compare
In the **model list**, check the box for each model you want to compare (you can choose up to 10).
Then, choose metrics to compare under **Metric Comparison**.
When you have at least two metrics selected, you can compare multiple metrics within a single model, the metric comparison loads as side-by-side comparison bar graphs. In the example above, the AutoML-generated model is used, and the `validation:accuracy`, `validation:macro\_recall`, and `validation:mlogloss` metrics are compared side-by-side.
