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The end result for every data labeling project is quality, usable data -- but how do you get there? There are several QA workflow types but each has pros and cons when it comes to the quality and speed of data outputs. In this panel discussion, we explore 5 popular quality assurance workflows for data labeling teams including tooling, staffing, and each impacts on throughput and data quality.
It takes a mountain of data to train, test, and build machine learning algorithms and AI projects. You may be considering hiring a data labeling service to take the burden off your in-house data scientists and machine learning engineers. But what does that entail? Watch the webinar to learn what you need to know before hiring a data labeling service.
Data-science tech developer Hivemind designed a quantitative experiment to determine which type of workforce completed a series of increasingly complex tasks to deliver the highest-quality structured datasets. Watch the webinar to learn which workforce performed better.
Data-science tech developer Hivemind designed a quantitative experiment to determine which type of workforce completed a series of increasingly complex tasks to deliver the highest-quality structured datasets. Watch the webinar to learn which workforce performed better.
