Run Python Code with Python API on New Prediction Made from BigML API

Pipedream makes it easy to connect APIs for Python, BigML and 2,400+ other apps remarkably fast.

Trigger workflow on

New Prediction Made from the BigML API

Next, do this

Run Python Code with the Python API

No credit card required

▶

Watch us build a workflow

8 min

Watch now ➜

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

Getting Started#

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

Select this integration
Configure the New Prediction Made trigger
1. Connect your BigML account
2. Configure timer
Configure the Run Python Code action
1. Connect your Python account
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#

New Prediction Made on BigML

Description:Emit new event for every made prediction. [See docs here.](https://bigml.com/api/predictions?id=listing-predictions)

Version:0.0.1

Key:bigml-new-prediction-made

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.

Trigger Code#

import base from "../common/base.mjs";

export default {
  ...base,
  key: "bigml-new-prediction-made",
  name: "New Prediction Made",
  description: "Emit new event for every made prediction. [See docs here.](https://bigml.com/api/predictions?id=listing-predictions)",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  methods: {
    ...base.methods,
    listingFunction() {
      return this.bigml.listPredictions;
    },
    emitEvent(prediction) {
      this.$emit(prediction, {
        id: prediction.resource,
        summary: `New prediction made: ${prediction.name}`,
        ts: prediction.created,
      });
    },
  },
};

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
BigML	`bigml`	`app`	This component uses the BigML app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger 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,

Navigate to your BigML account and sign in
Click on the Account menu on the top right
Go to “API key”

About BigML#

Machine Learning made beautifully simple. A company-wide platform that runs in any cloud or on-premises to operationalize Machine Learning in your organization.

Action#

Run Python Code on Python

Description:Write Python and use any of the 350k+ PyPi packages available. Refer to the [Pipedream Python docs](https://pipedream.com/docs/code/python) to learn more.

Version:0.0.2

Key:python-run-python-code

View on GitHub

Python Overview#

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Action Code#

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

export default {
  name: "Run Python Code",
  version: "0.0.2",
  key: "python-run-python-code",
  description: "Write Python and use any of the 350k+ PyPi packages available. Refer to the [Pipedream Python docs](https://pipedream.com/docs/code/python) to learn more.",
  props: {
    python,
  },
  type: "action",
  methods: {},
  async run({
    steps, $,
  }) {
    // Placeholder action for Python code app
  },
};

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
Python	`python`	`app`	This component uses the Python app.

Action Authentication#

The Python API does not require authentication.

About Python#

Anything you can do in Python can be done in a Pipedream Workflow. This includes using any of the 350,000+ PyPi packages available in your Python powered workflows.