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Synthetic data for AI and machine learning

Models are only as good as the data they were trained with. Synthetic data provides data for the training of models where otherwise no or only poor data would have been available. Furthermore, synthetic data can help to improve model performance.

AI/ML development challenges

AI and Machine Learning is hungry for data. However, data for training models is often hard to come by. Often only 15-20% of customers consent to having their data used for analytics. The rest of the data and the insights contained are locked away. Due to privacy reasons, sensitive data is often off-limits both for in-house data science teams and for external AI or analytics vendors.

Even when some data is available, data quality often is an issue. Missing relevant data complicate AI/ML development and negatively impact performance of models. Machine learning accuracy suffers when training data quality is insufficient. Training models on easy to access, fresh, balanced training data is not possible for most of the data scientists today.

The status quo in training data for machine learning and AI

The majority of AI/ML projects never make it into production due to the lack of high-quality training data. Most organizations do have the data, but it is locked up. Data owners are unwilling to or simply can't provide the necessary training data for privacy and compliance reasons. Even if data owners are on board, legacy anonymization used in data preparation often destroys data utility. Traditional anonymization techniques need to strip away the granularity of the data, ultimately leading to low quality models and suboptimal business decisions.

Synthetic training data for AI/ML development

In AI/ML development, synthetic training data is a great alternative to real data. Not only is it perfectly privacy-compliant, but due to the nature of the AI-powered synthesization process, the original data can be modified in certain ways.

For example, rare patterns and events can be upsampled in the synthetic training data, which can help ML performance to be significantly improved. Synthesization can also be used to generate more training data if the volume of training data was limited in the first place.

Case studies and guides

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