Creating and using synthetic data is easier than ever. This is particularly valuable for regulated industries with strict privacy laws.
To begin, separate your subject table – the information about your real-world customers, patients, employees, or other entities – into distinct rows. This will help the MOSTLY AI algorithm learn statistical patterns across unique subjects.
When creating machine learning models, data is a critical resource. However, acquiring enough real data can be difficult due to cost, sensitivity, and time constraints. To overcome these challenges, companies may choose to use synthetic data.
Conventional methods for generating synthetic data require the purchase of specialized tools or software and partnering with third parties that offer these services. These options are often expensive and require dedicated IT resources within the business.
Streamlined workflows are critical to the success of any project. A good solution must allow for a fast, easy, and flexible design process while supporting all of your business testing needs.
Synthetic data generation solutions must also support innovative use cases like fairness, privacy, and augmentation. These use cases are not well-suited to traditional open-source libraries, which only focus on standard data generation and lack quality control features. The synthcity library offers a solution to these problems by providing a collection of state-of-the-art generators in a modular, reusable, and composable way.
Unlike real-world data, synthetic data isn’t subject to privacy concerns and can be created more quickly. This enables companies to train and test AI models more efficiently and effectively. This is especially true for computer vision, a type of machine learning that requires vast amounts of image and video data.
Moreover, generating synthetic data is typically more cost-effective than collecting it from the real world. For example, it would take far longer for an automaker to acquire enough real-world images of vehicles driving around the world than it would use simulated data. Real-world image data also often must be manually labeled, a time-consuming and error-prone process.
Synthetic data can be generated and labeled with the click of a button, eliminating the need for an IT team. GenRocket also offers patented referential integrity across all permutations of data and tables. It is an ideal solution for safely testing AI models while preserving sensitive data and mitigating bias.
The use of synthetic data allows companies to create high-quality datasets for analytics, research, and machine learning without having access to confidential or sensitive information. This enables faster and more accurate decision-making and insights.
Companies often struggle to acquire enough real data for their machine-learning models within a reasonable timeframe. Generating the data by hand can be expensive and labor-intensive, but using synthetic data allows organizations to create large numbers of datasets quickly and accurately.
Synthetic data also offers reduced privacy risks, as unique values such as medical test results or banking transactions cannot be matched to individual individuals. However, there is still uncertainty about how to guarantee privacy in a way that retains data utility.
Today’s interconnected applications need dynamic data to simulate user and system interactions during the workflows they execute. For example, a testing environment needs to generate data in real time as users interact with the application during a complex workflow.
The best machine learning models are fueled by high-quality data, but this type of data isn’t always easy or inexpensive to source. It’s also prone to inaccuracies, errors, and bias that can severely compromise predictive performance. Synthetic data generation helps fill the gap by providing reliable, scalable data that can be generated faster and more affordably than real-world data.
Furthermore, generating synthetic data eliminates privacy concerns by replicating the characteristics and patterns of real-world data without exposing confidential information. This allows organizations to preserve data security while still allowing researchers, analysts, and decision-makers to gain valuable insights.
Moreover, creating fully synthetic data is a relatively straightforward process. This is due to the availability of different methodologies for generating synthetic data. These include a range of software and tools along with services provided by third parties. The global Synthetic Data Generation market is segmented based on Component, Deployment mode, Data type, Application, and Industry Vertical.