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1
Intro
2
Agenda
3
Labeled data: the missing pillar of Al
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ML production pipeline
5
Data labelling requirements
6
Crowdsourcing - ML
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Toloka platform
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Crowdsourcing for ML data labelling
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Instructions
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Interface
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Tolokers around the world
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Filters Toloka example
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Train your performers
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Behavior checks
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Fast responses example
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Quality checks
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Tips for control tasks
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Control tasks example
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Overlap and majority vote example
20
Pricing - Performance-based payment
21
Aggregation
22
Easy integration with other ML tools
Description:
Learn how to build effective data labeling pipelines for supervised machine learning projects through crowdsourcing in this 45-minute webinar. Explore real-life examples and best practices for obtaining high-quality labeled data that aligns with your specific problem. Discover the scalable approach of crowdsourcing across various domains, and gain insights into setting up instructions, interfaces, and quality control measures. Understand how to manage performers, implement behavior checks, and utilize pricing strategies for optimal results. Dive into topics such as aggregation techniques and integration with other machine learning tools to enhance your data labeling process.

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

Open Data Science
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