In the context of data labeling, a production project refers to projects specifically focused on the labeling of large datasets. Those projects involve managing and executing the annotation process for significant volumes of data. The labeled datasets are generated, then typically used for training and deploying machine learning models.
These projects are often carried out after a POC project or a training project, although it is possible to start with a production project right from the start (for example for simpler data annotation projects). Production batches are often larger than the training batch or pilot project.
When starting production, it is expected that all project parameters have been set, such as:
- The annotation tool and its settings
- The desired team size and its training (if necessary)
- The annotation process if necessary (review, consensus, etc.)
- The annotation instructions (they must cover “all” possible cases)
- The data output format
Overall, production projects in data labeling are focused on efficiently and effectively labeling large datasets to enable the training and deployment of machine learning models. Check out Batch or POC Project to know more!
Synonyms: Production batch
