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Data collection means pooling data by scraping, capturing, and loading from multiple sources including offline and online sources. High volumes of data collection or data creation can be the hardest part of a machine learning project, especially at scale.
Furthermore, all datasets have flaws. This is why data preparation is so crucial in the machine learning process. In a word, data preparation is a series of processes for making your dataset more machine learning-friendly. In a broader sense, data preparation also entails determining the best data collection mechanism. And these techniques take up the majority of machine learning time. It can take months for the first algorithm to be constructed
Obtaining the appropriate AI training data for your AI models can be difficult. TagX simplifies this procedure using a wide range of datasets that have been thoroughly validated for quality and bias. TagX can help you construct AI and ML models by sourcing, collecting, and generating speech, audio, image, video, text, and document data. We provide a one-stop-shop for web, internal, and external data collection and creation, with several languages supported around the globe and customizable data collecting and generation options to match any industrial domain need.
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