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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.

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Python

Integrate the BigML API with the Python API

Setup the BigML API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate BigML and Python remarkably fast. Free for developers.

Popular Ways to Connect BigML with Python

Run Python Code with Python API on New Model Created from BigML API
BigML + Python
 
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Run Python Code with Python API on New Prediction Made from BigML API
BigML + Python
 
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Popular BigML and Python Triggers

New Model Created from the BigML API

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

 
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New Prediction Made from the BigML API

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

 
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Popular BigML and Python Actions

Create Batch Prediction with the BigML API

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

 
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Run Python Code with the Python API

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

 
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Create Model with the BigML API

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

 
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Create Source (Remote URL) with the BigML API

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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Connect BigML

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    bigml: {
      type: "app",
      app: "bigml",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://bigml.io/andromeda/source`,
      params: {
        username: `${this.bigml.$auth.username}`,
        api_key: `${this.bigml.$auth.api_key}`,
      },
    })
  },
})

Overview of Python

Python API on Pipedream offers developers to build or automate a variety of
tasks from their web and cloud apps. With the Python API, users are able to
create comprehensive and flexible scripts, compose and manage environment
variables, and configure resources to perform a range of functions.

By using Pipedream, you can easily:

  • Create automated workflows that run on a specific schedule
  • Compose workflows across various apps and services
  • React to events in cloud services or form data
  • Automatically create content and notifications
  • Construct classifications and predictions
  • Analyze and react to sentiment, sentiment analysis and sentiment score
  • Connect backends to the frontend with serverless functions
  • Work with files and databases
  • Perform web requests and fetch data
  • Integrate third-party APIs into your apps
  • Orchestrate data processing tasks and pipelines
  • Create powerful application APIs with authentication and authorization
  • Design CI/CD pipelines and Continuous Delivery services
  • Connect databases like MongoDB and MySQL
  • Monitor connections and events
  • Generate alerts and notifications for corresponding events

Connect Python

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def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}
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