As AI applications increase in number and scope, the need for mass amounts of high-quality training data is rising. This poses a number of challenges, from scalability to flexibility to speed. Collaboration between humans and machines is the solution. By integrating machine learning models and automation tools into the annotation process and balancing them with the context and judgment that human annotators bring to the table — companies can achieve outstanding results.

What you’ll find:

-> An overview of the changing demands on data annotation
-> How machine learning models can improve data quality and data throughput
-> Automated annotation tools and how they can be best applied to scale manual annotation tasks
-> Results from ML-assisted data annotation projects