THE CHALLENGES OF
Data Collection and Annotation A lot of training data is required to develop an ML/AI model that functions as a human. The model must be trained to recognize specific objects to make decisions and take action. It is important to categorize and label the datasets for each use case properly. High-quality, human-powered annotation services can be used to enhance ML/AI algorithm
1. Managing large workforce ML and AI models require a lot of data to learn.
2. Cutting-edge technologies It takes a lot of skilled and experienced people to create high-quality, labeled data sets.
3. Falling short on consistent An accurate Data annotation platform demands high-quality data tagging
4. Not a cheap affair Annotating data is a tedious process.
Although it may seem appealing to use your data annotation services, it is not the best way to grow in the future.
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