The data labeling process for machine learning datasets is vital for the performance of the model. First of all, it would be essential to ascertain the type of data labeling; check if you are looking for data labeling at scale or want a dataset prepared for a pilot project.
Data labeling can be done manually if there are a few rows and the data is textual; to be used for testing the functioning of basic machine learning models. There are several open-source data annotation tools available for usage, which can be used for sorting data. If your data labeling needs are diverse then you will need to find a suitable data labeling tool.
However, if both data and the model with business objectives require professional-grade handling with right data labeling tools then approaching a data labeling solution provider is a good idea. Data labeling companies provide different kinds of annotation such as Bounding Box, Semantic Segmentation, Landmarking, Polygon, Polyline, 3D Cuboid, LIDAR Annotation. With these services, you can easily get highly-accurate datasets annotated or labeled as per the need. The best part is that you will be able to get customized data created, which can be directly used in machine learning without data cleansing required.