In-House vs Outsourcing Data Annotation for ML: Pros & Cons
In-house data annotation can be expensive but can be helpful in early stages of ML production. Outsourcing data annotation is cheaper but security can be compromised.
In-house data annotation can be expensive but can be helpful in early stages of ML production. Outsourcing data annotation is cheaper but security can be compromised.
Good annotation and testing practices are the foundations of building a great model. However, understanding what constitutes quality data is a tricky question.
Customers walk around stores, browse displays, walk down aisles, and stop to consider products. From the outside, it may not appear there is much to glean from these behaviors.
Here are some of the most frequently asked data labeling questions, along with recommendations for approaching your data annotation strategy holistically.
The purpose of a LiDAR annotation quality rubric is simple: it ensures that two people will score the same object in identical ways.
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Learn how Volumental partnered with Sama to accurately label the datasets that fuel the computer vision technology for their mobile foot scanning app.