Robust machine learning models rely on high-quality, high-dimensional, and high-fidelity data. However, data is a scarce resource, and obtaining it is often a challenge. Sensitive customer and private data are heavily regulated under data privacy laws, while public data is almost always biased, imbalanced, and incomplete.
This puts enterprises in a challenging position. Neither risk sensitive data breaches potentially leading to hefty fines and legal repercussions or invest significant resources into cleaning, organizing, and enriching public data.
The smart ones however take the third and right route i.e. Real Like Synthetic Data.
What is Synthetic Data?
At Betterdata, our differentially private synthetic data mimics real data's statistical properties, correlations, and nuances—without containing any personally identifiable information (PII).
No PIIs = No privacy risks = Unlimited, secure data usage and sharing.
Synthetic data is created through advanced ML models such as GANs, LLMs, VAEs, or DGMs. These models are trained on real data first and then generate synthetic data that looks, feels, and works exactly like real data.
This allows anyone generating synthetic data to,
- Augment synthetic data to remove biases and balance datasets.
- Enhance synthetic data to cover edge cases.
- Scale synthetic data to meet training data requirements for large-scale LLMs.
Data Augmentation with Synthetic Data for AI and ML:
1. Improved Model Generalization:
Synthetic data can be augmented to simulate a wide range of scenarios, helping AI and ML models generalize better. This reduces overfitting and improves the model’s ability to perform in real-world situations.
2. Cost-Effectiveness:
Creating synthetic data and augmenting it is more economical compared to collecting, cleaning, and annotating real-world data. This is particularly beneficial in resource-intensive domains such as healthcare, autonomous driving, or aerospace.
3. Balancing Imbalanced Datasets:
Synthetic data can be used to augment underrepresented classes in a dataset, addressing class imbalance. This ensures better model performance across all categories, particularly in use cases like fraud detection or medical diagnostics.
4. Simulating Rare and Extreme Scenarios:
Synthetic data can be tailored to replicate rare or extreme events, such as natural disasters for insurance models or rare defects for manufacturing quality control, enhancing the model’s robustness in handling edge cases.
5. Unlimited Scalability:
Synthetic data can be generated in virtually unlimited quantities, making it easy to scale datasets for training AI and ML models without the constraints of real-world data collection.
6. Faster Development Cycles:
With synthetic data, datasets can be created and augmented on demand, reducing delays caused by real-world data acquisition, cleaning, and labeling. This accelerates the overall AI and ML development process.
7. Domain-Specific Customization:
Synthetic data can be customized to meet the specific requirements of different industries or use cases, such as autonomous driving simulations, financial modeling, or natural language processing. This flexibility ensures models are trained with highly relevant data.
8. Improved Testing and Validation:
Synthetic data can augment datasets to include edge cases and rare conditions, providing a more comprehensive testing environment for models. This results in more reliable performance assessments.
9. Addressing Data Scarcity:
In scenarios where real-world data is scarce, such as emerging technologies or new product development, synthetic data provides a viable alternative to ensure AI and ML models are effectively trained.
Data augmentation with synthetic data is transforming the way organizations approach machine learning. By creating diverse, representative, and privacy-preserving datasets, enterprises can avoid models trained on flawed public datasets often ending up racist, sexist, or just plain wrong—leading to headlines we’d all rather avoid and enabling models to perform better in real-world scenarios.
