# Production Pipelines

## Generate Dataset From Scalar/Vector Values

Our production pipelines works strictly with your dataset in CSV or JSON files. This API is exposed to ease the generation of dataset from single values or vector of values to be used for prediction.

## Generate dataset API

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/models/generate`

To prepare a single value for prediction, pass the single value. To prepare for multiple values, pass them in an array. To prepare multi-dimensional values, pass them in nested arrays.

#### Path Parameters

| Name                                   | Type    | Description                              |
| -------------------------------------- | ------- | ---------------------------------------- |
| data<mark style="color:red;">\*</mark> | Various | Value(s) to be generated for prediction. |

{% tabs %}
{% tab title="200: OK URL to CSV for prediction use" %}

````
"To make an HTTP request in Python, you can use the built-in `requests` module. Here is an example:\n\n```python\nimport requests\n\nresponse = requests.get('https://www.example.com')\nprint(response.text)\n```\n\nThis code sends a GET request to `https://www.example.com` and prints the response content. You can also send other types of requests (POST, PUT, DELETE, etc.) by changing the method in the `requests` function. For example:\n\n```python\nimport requests\n\npayload = {'key1': 'value1', 'key2': 'value2'}\nresponse = requests.post('https://www.example.com/post', data=payload)\nprint(response.text)\n```\n\nThis code sends a POST request to `https://www.example.com/post` with a payload of `{'key1': 'value1', 'key2': 'value2'}` and prints the response content."
````

{% endtab %}
{% endtabs %}

## Make Predictions

## Perform quick and fully-managed model inference&#x20;

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/models/production/`

#### Request Body

| Name                                         | Type   | Description                              |
| -------------------------------------------- | ------ | ---------------------------------------- |
| test\_data<mark style="color:red;">\*</mark> | string | Dataset to be predicted                  |
| flow\_id                                     | String | Pipeline identifier to use for inference |

{% tabs %}
{% tab title="200: OK Inference results" %}

```
```

{% endtab %}
{% endtabs %}