A guide to data augmentation and data rebalancing

This white paper presents some of the problems that commonly arise in data science and machine learning teams, and shows how they can be easily tackled by making use of the Synthesized DataOps platform.

Data science projects present a unique array of challenges. The Synthesized DataOps platform can help to tackle many of them, including:

  • Predictive models trained on biased datasets can result in poor performance for organisations and their customers, damage customer experience and affect brand reputation. With the Synthesized platform, businesses can generate samples of an under-represented customer category to unbias the dataset and improve the model’s performance.
  • A combination of strict compliance regulations, questions of customer trust, and the limitations of data itself can lead to data shortage. Expanding a dataset with Synthesized improves a model's stability and performance.
  • Models can break in production if not properly validated due to population shifts. Unseen scenarios can be generated with Synthesized data manipulation, and used to test the model’s performance and ensure its stability.

Complete the form on the right to download the full white paper now.

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