The Synthetic Data Pipeline
Generate synthetic data within your protected IT infrastructure to produce machine learning-ready datasets, free from GDPR concerns. Accelerate innovation by enabling seamless collaboration among teams with this easily shareable data.
Fixed monthly fee - Unlimited synthetic data
Top Advantages of Synthetic Data
Syntethizing your data will improve security, time-to-market and AI performance.
Deploy locally
Deploy our synthetic data pipeline locally as a Docker image, ensuring that no individuals or organizations need access to your data. Additionally, there’s no requirement to transfer your information to a cloud provider, enhancing security and privacy. That makes the solution secure and provides fast deployment.
Privacy
Synthesizing your data allows you to create datasets free from personally identifiable information while preserving the essential signals for training AI models. This enables seamless sharing of synthetic data across teams and cloud providers, ensuring a secure connection between AI development skills and tools, and the data itself.
Innovate faster
Utilizing synthetic data empowers you to develop AI solutions without needing user consent or legal clearance, as the data is purely generated. This approach can significantly reduce the time from conception to implementation, potentially shaving months off the development process.
One model all data
Our synthetic data pipeline enables you to input metadata and features that guide the model to generate the precise data you require later on.
This also provides the flexibility to fine-tune the model for future data generation needs. As a result, you can swiftly delete the original data even before having a concrete plan for a specific model, ensuring data security and efficiency.
Finetuning
Generation
Synthetic Data Step by Step.
Step 1 | Remove persondata
The pipeline initiates by eliminating all personally identifiable information, ensuring that the model does not retain any details such as names, addresses, or similar data.
Step 2 | The model fine tunes on the data
Following that, the model fine-tunes itself using the data, capturing the underlying patterns across various combinations of features and metadata for optimal performance.
Step 3 | View performance indicators
Upon fine-tuning, the model is prepared to generate synthetic data. However, before proceeding, you’ll have the opportunity to evaluate how effectively the model captured the data’s underlying patterns. This assessment will help you determine if more data or fewer features are necessary for optimal results.
Step 4 | Generate synthetic data
You are now able to produce limitless quantities of synthetic data. You have the option to generate data that mirrors the distribution of your original dataset or to define a custom distribution. This capability allows you to minimize bias and enhance weak labels for a more robust dataset.