Why Synthetic Data
Synthetic data is increasingly recognized for its value in a variety of applications. It is particularly
useful for addressing gaps in datasets, which can occur due to incomplete data collection or
unavailability of real-world examples. By generating synthetic data, organizations can fill these gaps
and create more comprehensive and diverse datasets.
Synthetic data can also be used to balance datasets. This balancing act ensures that AI
models receive a more equitable distribution of examples, which can improve their performance and
accuracy. Synthetic data offers a broader range of
scenarios and conditions that may not be present in the original data. This diversity can enhance the
robustness of AI models and enable them to generalize better to new and unseen data.
While the
benefits of using synthetic data continue to fuel modern breakthroughs in fields, there are a bunch of risks that need to be assessed before
using Synthetic data, especially in more complex use cases.
The Risks
Using synthetic data comes with many risks, and can differ vastly in different scenarios and cases, here are a couple of risks that you might want to consider before using synthetic data:
- 1. Lack of Real-World Validity:
Synthetic data may not fully capture the complexity and nuances that real-world data represent. This could lead to models that perform well on synthetic data but fail when applied to actual real-world scenarios.
- 2. Overfitting and Bias:
Synthetic datasets are generated based on certain assumptions or models. If these models are flawed or overly simplistic, the synthetic data might introduce biases or fail to represent the diversity of real-world data.
- 3. Misuse and Ethical Concerns:
Synthetic data can be misused to deceive or manipulate. For example, it might be used to create misleading benchmarks or results in research or commercial applications.
Our Solution, Synthdat
Our approach to addressing the challenge of generating high-quality synthetic data is embodied in
Synthdat, a user-friendly synthetic data generator designed to simplify and enhance the process.
Synthdat leverages advanced, pre-trained AI models that operate locally on your machine, ensuring both
privacy and efficiency. These models have been trained on the original datasets to generate new synthetic datasets that mimic the
statistical properties and characteristics of the original dataset, providing a tool for data
augmentation and enrichment.
To develop Synthdat, we meticulously selected a diverse range of datasets and trained our AI models
using these examples. Among the models employed are those from the Synthetic Data Vault (SDV), renowned
for their robustness in generating realistic synthetic data. We rigorously evaluated the effectiveness
of these models by comparing key statistical measures of the original datasets with those of the
synthetic counterparts, ensuring that the newly generated data aligns closely with the original data in terms
of distribution and variance.
After many trials and tribulations, we are very excited to announce the official release of version 1.0.0 of Synthdat. This initial version is now available for download on Windows, macOS, and Linux platforms. We plan to continuously introduce new models and data generators to Synthdat. Stay tuned for future updates that will improve Synthdat...