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.
ML Assisted Annotation can help you generate high-quality pre-labeled and human-assisted annotations, for predictably higher quality data in half the time.
Announcing our support for custom keypoint shapes in our training data platform trusted by the world’s leading AI teams, for vector image and video annotation.