Synthetic data creation is the process of faking data that seem to be raw data collected from the real world. This process involved the creation of data that possess similar characteristics of actual data and acts as a model to real data and real life events. Secondary, synthetic data is suitable for training of machine learning models in situations where it would be difficult to gather large quantities of true data because of the factors such as privacy and cost like in the healthcare industry it is hard to come across data because of the HIPAA laws that govern the use of data. The issue here is that in real-life data may be scarce or expensive to accumulate. At times the synthetic data is integrated with the real data sets to enhance training of the models to better capabilities.
Reduces the risk of data breaches and ensures compliance with privacy laws
Reduces costs associated with data collection and annotation
Easily scalable to generate large datasets and adaptable to various scenarios.
Can be designed to mitigate biases present in real-world data.
Provides diverse training data, which can improve the robustness and accuracy of models.
However, synthetic data lacks realism and can be technically challenging and resource intensive and more often generating high quality synthetic data relies on availability of real data which could be limited.
datasets tailored to specific business needs.
Developing synthetic datasets that comply with privacy regulations like HIPAA, GDPR, and CCPA
Enhancing existing datasets with synthetic data to improve the training of machine learning models.
Designing synthetic data to address and reduce biases present in real-world datasets
Creating and implementing custom algorithms for generating high-quality synthetic data that closely mimics real-world data.
integrating synthetic data into existing data workflows and strategies..
training sessions and ongoing support for teams to effectively use and manage synthetic data
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