Carnegie Mellon University grad with experience at Google Brain and Amazon Robotics.
By its very nature, synthetic data sets come perfectly labeled. We also use new ML-techniques like domain randomization to improve model performance.
Data privacy laws are becoming more rigorous and restrictive. By using synthetic data, you no longer need to make the choice between utility and privacy when it comes to data.
We specialize in people problems: crowds and individuals. People come in all shapes and sizes. Your datasets should and can equally represent all ethnicities, races and genders.
Better AI starts with better data, and synthetic data is the very best—made to order.
At Zumo Labs, we generate custom synthetic data sets that result in more robust and reliable computer vision models. Unlike scraped and human-labeled data our data generation process produces pixel-perfect labels and annotations, and we do it both faster and cheaper.
What makes our data better?
First, the sheer volume of the sets we're able to create gives our synthetic data the potential to cover all edge cases and reduce bias in your model. We can generate complete data sets for any and all specific use cases you may have.
Second, we use machine learning techniques like domain randomization to close the sim2real gap. Our research shows that a domain randomized synthetic data set results in a significant performance boost over a real data set.
Finally, unlike real data that has simply been stripped of personal identifiers, re-identification is never possible with synthetic data, guaranteeing privacy and compliance with laws like CCPA and GDPR.
Zumo Labs is redefining the training data pipeline by putting the power of trusted, reliable synthetic data in your hands. Create a free account to generate a sample data set, and reach out to us at firstname.lastname@example.org to further discuss your specific use case.