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Challenge

AI-Driven Data Labeling Platform

Design and develop an app for an AI-powered platform that helps AI companies efficiently manage and scale data labeling projects. The platform should incorporate features such as task assignment, quality control, and payment processing. Consider using LLM APIs to intelligently categorize and route labeling tasks. The platform should be designed for scalability, handling different data types and labeling tasks.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Create a prototype AI-driven platform for managing data labeling projects.

Delivery guide

How work is evaluated

Evaluation

Evaluation will be based on the functionality, scalability, user experience, and code quality of the developed platform prototype.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Integrate NLP for intelligent task categorization.

  • Design a scalable architecture for the data labeling platform.

  • Develop a user-friendly interface for both data labelers and project managers.

  • Implement core features like task assignment, quality control, and payment processing.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

Challenge setup

Evaluation will be based on the functionality, scalability, user experience, and code quality of the developed platform prototype.

Challenge input

JSON containing project details, labeler profiles and available tasks

Agent execution

No agent tool is listed for this challenge.

Evaluated output

JSON containing assigned tasks with labeler assignments.

Evaluation checks
  • No evaluation checks are configured.
Configured evaluation evidence
  • Task Assignment Accuracy target: 90
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • No evaluation checks are configured.

Metrics

  • Task Assignment Accuracy · Target 90
  • Quality Control Effectiveness · Target 80

Workflow tasks

  1. Task 1

    Input: JSON containing project details, labeler profiles and available tasks

    Output: JSON containing assigned tasks with labeler assignments.

    Execution harness: Python

  2. Task 2

    Input: JSON containing labeled data

    Output: JSON highlighting quality issues and suggestions for improvement.

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AI-Driven Data Labeling Platform