MOSTLY AI
Synthetic Data Generation for Free Forever, up to 100K rows per day
Description:
MOSTLY AI is a synthetic data generation platform that provides high-quality, privacy-safe synthetic versions of your datasets for ML, advanced analytics, software testing, and data sharing. With MOSTLY AI, you can generate synthetic data that is statistically similar to your real data, but without the privacy risks. This makes it possible to share data more freely, collaborate more effectively, and develop better AI models.
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For Jobs:
Features
- Generate high-quality, privacy-safe synthetic versions of your datasets
- Share data more freely and collaborate more effectively
- Develop better AI models
- Accelerate your AI, analytics, and product development
- No code / API & Python Client
Advantages
- Privacy and security: Synthetic data points have no 1:1 relationship to the original data and provide much stronger privacy than traditional anonymization techniques.
- Flexibility: Synthetic data generation allows you to easily manipulate the data. Downsize large datasets into more manageable versions, blow up small datasets for stress testing systems, upsample minority classes for more accurate machine learning models, perform data simulations by changing distributions, or fill in missing data with realistic synthetic data points.
- Intelligence: Real data always comes with significant limitations. Legacy data anonymization techniques destroy the utility and the intelligence of your real data. Synthetic data generation provides higher privacy and high utility.
- Accuracy: MOSTLY AI's synthetic data is the most accurate on the market today.
- Ease of use: MOSTLY AI's no-code UI makes it easy to generate synthetic data, even for non-technical users.
Disadvantages
- Can be expensive to generate large amounts of synthetic data
- May not be suitable for all types of data
- Can be difficult to ensure that synthetic data is representative of the real world
Frequently Asked Questions
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Q:What is synthetic data?
A:Synthetic data is an artificial version of your real data that looks and feels like real data. It can be used for a variety of purposes, such as training machine learning models, testing software, and developing new products. -
Q:Why use synthetic data?
A:There are many benefits to using synthetic data, including: - Privacy: Synthetic data does not contain any personal information, so it can be shared more freely. - Flexibility: Synthetic data can be easily manipulated, so it can be used to create a variety of different scenarios. - Accuracy: Synthetic data can be generated to be very accurate, so it can be used to train machine learning models that are more accurate. -
Q:How do I generate synthetic data?
A:There are a number of different ways to generate synthetic data. One common method is to use a generative adversarial network (GAN). GANs are a type of neural network that can learn to generate new data that is similar to real data.
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