Pros and Cons of In-House vs Outsourced Data Annotation Platform • •
A staggering 1.145 trillion MB of data is created per day. That’s a lot, right? But did you know, almost 95% of this data is unstructured?
Can this data be used for training AI models? Absolutely not! Because for data to be used in training AI and ML algorithms, it needs to be structured, labeled, and classified. Solution? It’s DATA ANNOTATION! The data annotation tool is used to tag, label, or annotate this unstructured data for training AI/ML models. And because of the undeniable benefits of data annotation in redefining businesses, it has taken center stage in technological innovations. The next question that might run in your head is whether to build an in-house team or outsource your data annotation requirements? Let’s try and find an answer to this question. Creating an In-House Data Annotation Team - With Your Skilled Workforce! •
Pros Building your in-house data annotation team can be an ideal option when the volume of data to be annotated is less, and the cost of getting it outsourced is burning a hole in your pocket. An in-house data annotation platform can allow you to choose the tool or build it in-house according to your business needs and requirements. Moreover, having physical proximity helps have direct supervision of the complete data annotation solutions. Lastly, one crucial reason that businesses choose in-house data labeling over outsourcing is the issue of security. Lately, outsourcing vendors have understood the sensitivity of the problem and therefore follow stringent confidentiality protocols. However, some businesses still feel reluctant to share their confidential images and information over the internet.
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