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Webinar

How to Choose your Data-Labeling Solution?

 Insider tips on how to find the data labeling solution that fits your organization's specifics

October 27th, 2022  - 5:00 p.m CEST / 11:00 a.m EDT - English

Register to attend

“Open-source? Buy or Build? Internal or outsourced? How to find the data labeling that actually fits my needs?”

Day after day, organizations like yours share their needs and pains around labeling solutions with us. We understand that finding the path toward the right solution can be challenging. That’s why we have developed a framework to help you navigate the labeling ecosystem and find the right solution to fit your needs.

Join our webinar on how to choose your data labeling solution, and discover our insider insights on how to find the data labeling solution that fits your organization's specifics!

On October the 27th, at 5:00 p.m CET - 11:00 EDT, we will share insights on:

  • the data labeling ecosystem (Open Source, Labeling Service Provider, build or buy Data Labeling Platform);
  • criteria and features to consider;
  • the framework to find the data labeling solution most likely to suit your company.
Stéphanie Nguyen

Stéphanie Nguyen
Product Manager
@ Kili Technology

MvM


Michael Van Meurer
Pre-Sales Engineer
@ Kili Technology

"More and more products are
powered by machine learning.
That’s why it's [capital] to think about ethics and to make sure
[its] impact [is] positive.“

Clément Delangue
Co-founder & CTO
@Hugging Face

"Our Data-centric approach to AI
has helped us achieve a categorization of our own categories that is accurate in more than ninety-five percent
of the videos"

Andrea Colonna
VP AI
@Jellysmack

But the real-world experience of those who put them into production shows that (...) it's often the quality of
data (...) that makes your AI project succeed or fail.”

Edouard D'Archimbaud
Co-founder & CTO
@Kili Technology

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Labeling Platform for High-quality Data

One tool to label, find and fix issues, simplify DataOps,

and dramatically accelerate the build of reliable AI

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