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YouTube

How to Set Up an ML Data Labeling Pipeline - Best Practices and Examples

Open Data Science via YouTube

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Overview

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This webinar explains how to build machine-learning data-labeling pipelines through crowdsourcing. It covers performer selection and training, quality control, aggregation, pricing, and integration with machine-learning tools.

Syllabus

Intro
Agenda
Labeled data: the missing pillar of Al
ML production pipeline
Data labelling requirements
Crowdsourcing - ML
Toloka platform
Crowdsourcing for ML data labelling
Instructions
Interface
Tolokers around the world
Filters Toloka example
Train your performers
Behavior checks
Fast responses example
Quality checks
Tips for control tasks
Control tasks example
Overlap and majority vote example
Pricing - Performance-based payment
Aggregation
Easy integration with other ML tools

Taught by

Open Data Science

Reviews

4.2 rating, based on 4 Class Central reviews

Start your review of How to Set Up an ML Data Labeling Pipeline - Best Practices and Examples

  • Profile image for Ruan Du Preez
    Ruan Du Preez @ruandupreez
    1
    I learned some new things, received some helpful content, and got back on track with some old, at least another one under my belt.
  • Gody
    The class is quite interesting and educational but some practical hands on training and assignments will also be good
  • Profile image for Marcos Vinicius
    Marcos Vinicius
    Very good course, easilly understandble. Managed to get through the essentials.

    Good for enhancing learning in the AI field.
  • Profile image for Sheepdog
    Sheepdog
    great course very informative and very clear it was build for someon who was new to ML and labelling
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