Create Batch Prediction with BigML API on Monthly Schedule from Schedule API

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Trigger workflow on

Monthly Schedule from the Schedule API

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Create Batch Prediction with the BigML API

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Getting Started#

This integration creates a workflow with a Schedule trigger and BigML action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the Monthly Schedule trigger
1. Connect your Schedule account
2. Configure Cron Schedule
Configure the Create Batch Prediction action
1. Connect your BigML account
2. Optional- Select a Model ID
3. Optional- Select a Dataset ID
4. Optional- Configure Description
5. Optional- Configure Other Arguments
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

Monthly Schedule on Schedule

Description:Trigger your workflow on one or more days each month at a specific time (with timezone support).

Version:0.0.2

Key:schedule-monthly

View on GitHub

Schedule Overview#

The Schedule app in Pipedream is a powerful tool that allows you to trigger workflows at regular intervals, ranging from every minute to once a year. This enables the automation of repetitive tasks and the scheduling of actions to occur without manual intervention. By leveraging this API, you can execute code, run integrations, and process data on a reliable schedule, all within Pipedream's serverless environment.

Trigger Code#

import schedule from "../../schedule.app.mjs";

export default {
  name: "Monthly Schedule",
  version: "0.0.2",
  key: "schedule-monthly",
  type: "source",
  description: "Trigger your workflow on one or more days each month at a specific time (with timezone support).",
  props: {
    schedule,
    cron: {
      propDefinition: [
        schedule,
        "cron",
      ],
      default: {
        cron: "0 10 1 * *",
      },
    },
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
Schedule	`schedule`	`app`	This component uses the Schedule app.
Cron Schedule	`cron`	`$.interface.timer`	Enter a cron expression

Trigger Authentication#

The Schedule API does not require authentication.

About Schedule#

Trigger workflows on an interval or cron schedule.

Action#

Create Batch Prediction on BigML

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)

Version:0.0.1

Key:bigml-create-batch-prediction

View on GitHub

BigML Overview#

The BigML API offers a suite of machine learning tools that enable the creation and management of datasets, models, predictions, and more. It's a powerful resource for developers looking to incorporate machine learning into their applications. Within Pipedream, you can leverage the BigML API to automate workflows, process data, and apply predictive analytics. By connecting BigML to other apps in Pipedream, you can orchestrate sophisticated data pipelines that react to events, perform analyses, and take action based on machine learning insights.

Action Code#

import bigml from "../../bigml.app.mjs";
import { parseJSON } from "../../common/utils.mjs";

export default {
  key: "bigml-create-batch-prediction",
  name: "Create Batch Prediction",
  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)",
  version: "0.0.1",
  type: "action",
  props: {
    bigml,
    modelId: {
      propDefinition: [
        bigml,
        "modelId",
      ],
      optional: true,
    },
    datasetId: {
      propDefinition: [
        bigml,
        "datasetId",
      ],
      optional: true,
    },
    description: {
      type: "string",
      label: "Description",
      description: "A description of the batch prediction",
      optional: true,
    },
    args: {
      type: "object",
      label: "Other Arguments",
      description: "Other arguments for the batch prediction. [See the docs](https://bigml.com/api/batchpredictions?id=batch-prediction-arguments) for more information",
      optional: true,
    },
  },
  async run({ $ }) {
    const response = await this.bigml.createBatchPrediction({
      $,
      data: {
        ...parseJSON(this.args),
        model: this.modelId,
        dataset: this.datasetId,
        description: this.description,
      },
    });
    $.export("$summary", "Succesfully created batch prediction");
    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
BigML	`bigml`	`app`	This component uses the BigML app.
Model ID	`modelId`	`string`	Select a value from the drop down menu.
Dataset ID	`datasetId`	`string`	Select a value from the drop down menu.
Description	`description`	`string`	A description of the batch prediction
Other Arguments	`args`	`object`	Other arguments for the batch prediction. See the docs for more information

Action Authentication#

BigML uses API keys for authentication. When you connect your BigML account, Pipedream securely stores the keys so you can easily authenticate to BigML APIs in both code and no-code steps.

To retrieve your API key,