How Tooling and Workflows Are Crucial for Data Annotation Quality

As Artificial Intelligence continues to boom, improved technologies for data labeling and annotation are on the rise. From robotic perception and manipulation to self-driving cars, Machine Learning typically requires millions of data points to be annotated.

According to analyst firm Cognilytica, the market for AI and Machine Learning relevant data preparation solutions is expected to grow to $3.5B by the end of 2024. To keep up with this growing demand, data labeling providers are thinking strategically about the ways in which annotation workflows, tooling capabilities and workforces can scale, with accuracy and precision.

Here are some of the latest workflow improvements that will make the process more efficient and faster:

  • Predictive Annotation Tools
  • Customized Reporting
  • Focus on Quality Control
  • Workforce of Experts
  • Specialized Partner Ecosystem
Read more in this blog post
top 5 Infographics

If you wish to learn more about creating training data sets for Machine Learning, please contact us to talk to an expert.