Our simulation platform generates synthetic data to train and improve machine learning algorithms.
The success of deep learning, a way to approach machine learning, has brought an insatiable hunger for data. The performance of deep learning often correlates with the amount of data used in training. However, data at scale is often proprietary, expensive, and laborious for people to manually prepare. The best way to deal with these challenges is with synthetic data — data created in virtual worlds rather than collected from the real world.
With synthetic data, a fully scalable data solution is now at our fingertips — at a fraction of the cost. By simulating the real world, virtual worlds create synthetic data that is as good as, and sometimes better than, real data.