Synthetic data can augment limited datasets
#FASTCUT SHARED DATABASE MANUAL#
Synthetic data opens up the possibilities of enabling access to artificial and privacy-preserving versions of personal data in minutes, with 95% of the accuracy of the real-world data it was trained on, and without having to wait weeks for manual anonymization and approvals. Developers and data scientists don’t always need - or even want - access to sensitive or personally identifiable information (PII). Source: Kaggle user survey Synthetic data offers faster access to sensitive dataįrom our experience working at AWS, Google, OpenAI, and with other leaders in the data industry, we know first hand that enabling developers to safely learn and experiment with data is the key to rapid innovation. Time spend on different aspects of a typical data science project. In fact, in a recent Kaggle survey, 20,000 data scientists listed the “data gathering” stage as the single most time-consuming part of a typical project, accounting on average for 35% of the total work. One of the biggest bottlenecks to innovation that developers and data scientists face today is getting access to data, or creating the data needed to test an idea or build a new product. With the right tools, synthetic data is also easy to generate, so it is considered a fast, cost-effective data augmentation technique, too. Research has shown that synthetic data can be as good or even better than real-world data for data analysis and training AI models and that it can be engineered to reduce biases in datasets and protect the privacy of any personal data that it’s trained on. More specifically, it is artificially annotated information that is generated by computer algorithms or simulations. Synthetic data is commonly used as an alternative to real-world data. Synthetic Data: Artificial Data, Actual Events In this blog, we’ll cover what synthetic data is, how it’s made, its various types and benefits, and why developers, data scientists and enterprise teams across industries are eager to use it. What if you could have instant access to an unlimited supply of high-fidelity data that’s statistically accurate, privacy-protected and safe to share? That’s the promise of synthetic data.
0 kommentar(er)
