One example is San Francisco-based Synthesis AI’s synthetic human face dataset, comprising 5,000 individual images of diverse human faces. Snowflake sells data to businesses via its Snowflake marketplace, which is one of the largest B2B data brokerages in the world.Īlongside its thousands of real-world datasets, Snowflake now offers access to synthetic datasets created by generative AI algorithms. But when used effectively, it can reduce the cost, speed up the training of machine learning models, and help businesses automate and make better decisions. It doesn’t negate the need for real-world data, which is needed to create synthetic data in the first place. It also found that partially synthetic datasets – where real-world data is augmented with synthetic data – are more commonly used than fully synthetic datasets.īy generating synthetic data, companies can create any information they need to plug gaps in existing records or create entirely new datasets. ![]() It’s used in finance to train fraud detection algorithms to spot deliberately falsified transactions, in healthcare to avoid using sensitive patient data, and in retail and marketing to create synthetic customers and analyze their buying behavior.Īccording to Gartner research, business leaders are most likely to turn to synthetic data because of difficulties with accessibility, complexity and availability of real-world data. ![]() It means businesses can train AI algorithms and perform tests and simulations without exposing private or sensitive information that might be contained in real-world data. Generative AI is particularly suited to this task as it can easily analyze any dataset and then create synthetic data that closely matches it.
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