# Create Batch Prediction — BigML

> Create a batch prediction given a Supervised Model ID and a Dataset ID. See the docs.

- Key: `bigml-create-batch-prediction`
- Type: Action (Write)
- Version: 0.0.2
- App: BigML (`bigml`) — https://pipedream.com/apps/bigml.md
- This page (HTML): https://pipedream.com/apps/bigml/actions/create-batch-prediction
- Hints: open-world
- Source: https://github.com/PipedreamHQ/pipedream/blob/master/components/bigml/actions/create-batch-prediction/create-batch-prediction.mjs

## Description

Create a batch prediction given a Supervised Model ID and a Dataset ID. [See the docs.](https://bigml.com/api/batchpredictions?id=creating-a-batch-prediction)

## Props

| Prop | Type | Required | Description |
|---|---|---|---|
| `modelId` | `string` | No | The ID of the model Options are loaded from the connected account. |
| `datasetId` | `string` | No | The ID of the dataset Options are loaded from the connected account. |
| `description` | `string` | No | A description of the batch prediction |
| `args` | `object` | No | Other arguments for the batch prediction. See the docs for more information |

## Run it

**MCP**

```ts
import { Client } from "@modelcontextprotocol/sdk/client/index.js"
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js"
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const accessToken = await pd.rawAccessToken

const transport = new StreamableHTTPClientTransport(
  new URL("https://remote.mcp.pipedream.net/v3"),
  {
    requestInit: {
      headers: {
        Authorization: `Bearer ${accessToken}`,
        "x-pd-project-id": process.env.PIPEDREAM_PROJECT_ID!,
        "x-pd-environment": "production",
        "x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
        "x-pd-app-slug": "bigml",
      },
    },
  },
)

const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)

const { tools } = await mcp.listTools()

// listTools() hands your model this tool's input schema, so it can
// fill the arguments itself:
const result = await mcp.callTool({
  name: "bigml-create-batch-prediction",
  arguments: {
    modelId: "Model ID",
    datasetId: "Dataset ID",
  },
})
```

**TypeScript**

```ts
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const result = await pd.actions.run({
  id: "bigml-create-batch-prediction",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    bigml: { authProvisionId: "apn_xxxxxxx" },
    modelId: "Model ID",
    datasetId: "Dataset ID",
  },
})

console.log(result)
```

**cURL**

```bash
curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
  -H "Content-Type: application/json" \
  -H "X-PD-Environment: production" \
  -H "Authorization: Bearer {access_token}" \
  -d '{
    "external_user_id": "{external_user_id}",
    "id": "bigml-create-batch-prediction",
    "configured_props": {
      "bigml": { "authProvisionId": "apn_xxxxxxx" },
      "modelId": "Model ID",
      "datasetId": "Dataset ID"
    }
  }'
```

---

- App: https://pipedream.com/apps/bigml.md · All apps: https://pipedream.com/apps