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Connect BigML to Python

Use 2 documented BigML triggers to run 1 documented Python actions.

2 source triggers
1 target actions

Why Connect BigML & Python?

Start with a BigML event

Choose from 2 documented triggers, including New Model Created, New Prediction Made.

Run a Python action

Send the event into one of 1 documented actions, such as Run Python Code.

Map and review the workflow

Choose the fields and conditions for the workflow, then test the result before activation.

Available Triggers & Actions

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BigML

Triggers (2)

New Model Created

Emit new event for every created model. See docs here.

polling
New Prediction Made

Emit new event for every made prediction. See docs here.

polling

Actions (3)

Create Batch Prediction

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

Create Model

Create a model based on a given source ID, dataset ID, or model ID. See the docs.

Create Source (Remote URL)

Create a source with a provided remote URL that points to the data file that you want BigML to download for you. See the docs.

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Python

Actions (1)

Run Python Code

Write Python and use any of the 350k+ PyPi packages available. Refer to the Mazaal AI Python docs to learn more.

Integration Features

2 documented BigML triggers
1 documented Python actions
Configurable field mapping
Conditional workflow steps
Execution monitoring

Capability Examples

BigML trigger → Python action

Catalog example
BigMLBigML

New Model Created

PythonPython

Run Python Code

How It Works

1

Connect Apps

Authenticate your BigML and Python accounts

2

Choose a Trigger

Select a documented BigML event

3

Choose an Action

Select the documented Python operation to run

Test and Review

Verify field mapping and provider responses before activation

Frequently Asked Questions

Integration Benefits

Trigger-driven Workflows

Start the supported direction from one of 2 documented BigML triggers.

Automated Actions

Run one of 1 documented Python actions after the selected trigger fires.

Provider Authentication

Review the authentication requirements published by each provider before activating the workflow.

Guided Configuration

Select a documented trigger, choose a target action, and map the fields required by the workflow.

Field Mapping

Map the fields exposed by the selected trigger and action, then review the required provider inputs.

Execution Review

Test the workflow and review provider responses before relying on it in production.

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All Python Integrations

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Ready to Connect BigML & Python?

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