20 Creative AI Tools for Creating Synthetic Data
The artificial intelligence revolution we are currently experiencing is a direct result of this explosion in the volume of data available to be mined and analyzed for insights.
However, collecting data from the real world can be difficult. Storing and working with personal data creates privacy and security challenges, while other types of data can be expensive or even dangerous.
So why not create artificial data that is close enough to real-world data that it can be used for many of the same purposes at a fraction of the cost in terms of time, money and risk? That’s the promise synthetic data – another area where genetic AI is quickly becoming a valuable tool.
Here’s a rundown of some of the most useful, interesting, or unique AI generation tools designed to generate synthetic data, including free and paid tools:
Primarily, it is an established synthetic data platform for generating data that closely mimics the real world. It is used in industries such as finance, retail, telecommunications and healthcare. Highlighted as a Cool Vendor by Gartner, it stands out by enabling the creation of data sets that guarantee privacy and compliance with data protection regulations such as GDPR and CCPA. Its user interface is built around natural language, which means the data it generates can be queried in the same way you would chat with a bot like ChatGPT. It also includes guardrails to protect against introducing bias into the synthetic data it generates.
Gretel makes it easy for almost anyone to create tabular, unstructured, and time series data for use in any type of analytics or machine learning workflow. It is designed to be simple to use, allowing the creation of synthetic data with little coding experience. A large number of plug-ins and API integrations make it compatible with most cloud and data warehouse infrastructures, and a community of active users is available for help and support.
Synthea is a free-to-use, open-source tool specifically designed for generating synthetic patients for use in healthcare analyses. It can generate entire medical records of patients that may not exist, but could still hold clues to solving difficult healthcare problems. This means that medical researchers can carry out their work without having to worry about the privacy or ethical issues of working with real patient data.
A comprehensive platform for developing realistic, compliant and secure synthetic data, Tonic is built primarily for software and artificial intelligence development. In addition to generating synthetic data, it offers de-identification to anonymize real-world data. It can be deployed on-premises or accessed in a cloud environment and is designed to integrate with all commonly used databases.
Faker is a library available for Python and JavaScript, as well as many other languages, rather than a standalone tool, so it requires some coding knowledge. However, it is a popular tool with users looking to fake data ranging from e-commerce shopping habits to financial transactions. This data can then be used to train anything from recommendation engines to fraud detection algorithms, without the risk of compromising privacy that comes with using real data.
More Generative AI Tools for Synthetic Data
In addition to the five tools described above, here are others worth checking out:
It allows the creation of very technical and complex datasets.
Simplifies data masking and anonymization by generating synthetic data for businesses.
Computer vision and video analytics supported by synthetic data.
Create datasets with dynamic validation tools to ensure they are as realistic as possible.
Create labeled synthetic data as a service.
Dynamic data generation with enterprise scalability, aimed at generating data for software testing.
It recently relaunched as the world’s first synthetic data marketplace.
Generates data for the purpose of training machine learning models.
Code-free data augmentation designed to improve privacy and improve the performance of neural networks.
Synthetic data aimed at healthcare professionals.
Synthetic training data generator for computer vision applications.
Billed as a “data enhancer,” it mimics real data sets by mapping the attributes and correlations of existing data.
Open source machine learning model for generating large volume synthetic data.
Create self-service data for information and decision making.
Automated synthetic data generation to improve AI model productivity and performance.
