> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Local File Upload

> Drag & Drop a file through the Arize UI

## Supported File Types

Arize supports CSV, Parquet, and Avro. The required [model schema parameters](/ax/machine-learning/machine-learning/how-to-ml/upload-data-to-arize/ui-drag-and-drop#local-file-upload-model-schema) vary based on the specific model type selected. Learn more about model types [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/utils.types.modeltypes).

<Info>
  `embeddings_features` and `list_of_strings` are available for Parquet and Avro files.
</Info>

## How To Upload A Local File

Uploading a local file is as simple as dropping a file into the upload container and can be broken down into a few easy steps.

* Go to the 'Upload Data' page from the left-side navigation and click on Local Files

<Frame caption="Select 'Local Files' from Upload Data Page">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/e7613db0-image.jpeg" />
</Frame>

* You can get started by choosing the model type and downloading a sample file.

  * [Regression Model Sample File](https://storage.googleapis.com/arize-assets/fixtures/LocalUploadExamples/local_upload_regression_model.csv) [Download](https://storage.googleapis.com/arize-assets/fixtures/LocalUploadExamples/local_upload_regression_model.csv)

  * [Score Model Sample File](https://storage.cloud.google.com/arize-assets/fixtures/Tags-Demo-Data/churn_prediction_production.csv) [Download](https://storage.cloud.google.com/arize-assets/fixtures/Tags-Demo-Data/churn_prediction_production.csv)

* Upload a file from your desktop by either:

  * Dragging the file into the upload container

  * Clicking 'Select File'

<Frame caption="Drag file or click 'Select File'">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/16c7c268-image.jpeg" />
</Frame>

* Fill out model schema fields using [this](/ax/machine-learning/machine-learning/how-to-ml/upload-data-to-arize/ui-drag-and-drop#local-file-upload-model-schema) schema reference

<Frame caption="Fill out model schema">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/587b00ea-image.jpeg" />
</Frame>

* Validate your file; errors will be displayed on this page

<Frame caption="Validate file">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/e71c19ff-image.jpeg" />
</Frame>

* 'Start Job' to create a file import job for your local file

  * You'll be taken to the Job Status tab to see your local file uploads

<Frame caption="Successful local file import">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/115ea625-image.jpeg" />
</Frame>

* Go to Job Status Tab to see your local file uploads

<Frame caption="Job Status of All Local File imports">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/cc5cd015-image.jpeg" />
</Frame>

## Local File Upload Model Schema

Model schema parameters are a way of organizing model inference data to ingest to Arize. When configuring your schema, be sure to match your file column headers with the model schema. Use **a form** or a simple **JSON-based schema** to specify the column mapping.

#### Example Row

| prediction\_id | prediction\_ts | user\_id | feature/metropolitan\_area | industry    | prediction\_score | actual\_score | prediction\_label | actual\_label | tag/zone  |
| -------------- | -------------- | -------- | -------------------------- | ----------- | ----------------- | ------------- | ----------------- | ------------- | --------- |
| 1fcd50f4689    | 1637538845     | 82256    | 1PA                        | engineering | 0.07773696        | 0             | No Claims         | No Claims     | us-east-1 |

#### Example Schema

```javascript theme={null}
{
  "prediction_id": "prediction_id",
  "timestamp": "prediction_ts",
  "features": "feature/", #with explicit features (must pick explicit or implicit)
  "prediction_score": "prediction_score",
  "prediction_label": "prediction_label",
  "actual_score": "actual_score",
  "actual_label": "actual_label",
  "tags": "tag/",
  "shap_values": "shap/",
  "exclude": ["user_id"]
}
```

#### Schema Parameters

Learn more about Schema fields [here](/ax/machine-learning/machine-learning/how-to-ml/upload-data-to-arize/ui-drag-and-drop#local-file-upload-model-schema). Refer [here](/ax/machine-learning/machine-learning/how-to-ml/upload-data-to-arize/sending-data-faq#what-are-the-expected-data-types-for-my-file-type) for a list of the expected data types for Schema fields by file type.

<Info>
  We currently support uploading one file at a time, and a maximum file size of 300 mb.

  To upload multiple files or larger files, use one of our [data connectors](https://arize.com/docs/ax/machine-learning/machine-learning/integrations-ml).
</Info>