Integrate the Scale AI API with the Python API

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

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Popular Scale AI and Python Actions#

Create Document Transcription Task with the Scale AI API

Create a document transcription task. See the documentation

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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 Image Annotation Task with the Scale AI API

Create an image annotation task. See the documentation

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Create Text Annotation Task with the Scale AI API

Create a text annotation task. See the documentation

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Overview of Scale AI#

Scale AI offers an API to automate and streamline data labeling for machine learning applications, providing access to a global workforce and sophisticated tools. With Scale AI's API on Pipedream, you can integrate scalable data annotation workflows directly into your apps. Trigger tasks, manage datasets, and receive annotated data, all within Pipedream's serverless platform. This enables seamless automation of labeling tasks, integration with machine learning pipelines, and real-time updates on annotations.

Connect Scale AI#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    scale_ai: {
      type: "app",
      app: "scale_ai",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.scale.com/v1/teams`,
      headers: {
        "Accept": `application/json`,
      },
      auth: {
        username: `${this.scale_ai.$auth.api_key}`,
        password: ``,
      },
    })
  },
})

Overview of Python#

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:

Connect Python#

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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