четверг, 12 мая 2022 г.

How an efficient workforce can influence data labeling success.

Greetings

Researchers have found that carefully curated datasets addressing model performance issues can yield more significant improvements.

As ML projects grow in scope and complexity, locating high-quality training data can become more challenging. Moreover, reviewing your labels and iterating on them is essential to producing high-quality training data. However, it may require additional labeling workflows that can be challenging, resulting in even teams with sufficient training data encountering obstacles to improving the model's performance.

Because of this, MarsCrowd gathered an expert crowd workforce, program management, and annotation tools that can streamline the entire process of data annotation from beginning to end. 

Are you available for a https://meetings.hubspot.com/shabbir3/queries-and-services-discussion to discuss your current challenges so that we can determine a suitable solution? 



Best wishes,
Shabbir Dhillawala | Business Development Manager
EN ISO 17100:2015, EN 15038:2006 Certified
ISO 9001:2015 Certified Language and IT Services provider
 
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