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Learn what the super.AI Data Processing Crowd is, how it works, and how it can transform the way your business automates data processing at scale.
With super.AI you can use our in-house labelers or you can choose to label data yourself or with team members you invite. The option you choose depends on your project’s requirements.
The world of machine learning (ML) is complicated. If you’re thinking about integrating ML into your project, there are hundreds of questions you will quickly find yourself asking. How to store, version, and process your data? What development framework and hyperparameter optimization and resource management solutions should you use during the development and training of your model?
To create training data for ML projects, you need to work with human labelers. That means writing clear instructions on how to label your data. Here’s 12 rules for how to do this right.