Gartner predicts that by 2030, machine learning models will use synthetic data exclusively, making real data unnecessary. The synthetic data market is multiplying, with Cognilytica forecasting an increase from $110 million in 2021 to an impressive $1.15 billion by 2027.
Synthetic data is generated to mimic real-world data. It’s created using algorithms, simulations, or predefined rules. Today, it is primarily used in:
research
training machine learning algorithms
data analysis
testing software products
We collected a list of companies you can use to generate synthetic data:
Gretel: Offers data anonymization solutions with APIs and machine learning models for data integration and analysis.
Mostly AI: Specializes in generating high-quality synthetic data for various industries, focusing on privacy and diverse applications.
Hazy: Focuses on synthetic data generation emphasizing data privacy and compliance across healthcare, banking, and technology.
Tonic AI: Provides automated synthetic data creation for testing and development, ensuring privacy and compliance.
Datagen: AI-based platform for generating synthetic data, particularly for training computer vision models.
Synthesis: Offers a data generation platform for computer vision, with an API for programmatic image generation.
betterdata: Cloud-based data security management platform offering AI-based solutions for data protection and anonymization.
Rendered: Provides AI and cloud-based tools for producing synthetic datasets, with applications in various industries.
GenGenAI: AI-based platform for data preparation, specializing in generating and transforming image and video data.
Vypno: Offers AI-based synthetic data for object recognition in images from various sources.
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