DataGen
DataGen is a leading AI platform specializing in synthetic data generation and custom generative AI models for machine learning projects. Their flagship product, SynthEngyne, supports multi-format data generation including text, images, tabular, and time-series data, ensuring privacy-compliant, high-quality training datasets. The platform offers scalable, real-time processing and advanced quality controls like deduplication to maintain dataset fidelity. DataGen also provides professional AI development services such as model deployment, fine-tuning, synthetic data consulting, and intelligent automation systems. With flexible pricing plans ranging from free tiers for individuals to custom enterprise solutions, DataGen caters to a wide range of users. Their solutions serve diverse industries including healthcare, finance, automotive, and retail.
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DataCebo Synthetic Data Vault (SDV)
The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables, or sequential tables. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights. Control data processing to improve the quality of synthetic data, choose from different types of anonymization, and define business rules in the form of logical constraints. Use synthetic data in place of real data for added protection, or use it in addition to your real data as an enhancement. The SDV is an overall ecosystem for synthetic data models, benchmarks, and metrics.
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Symage
Symage is a synthetic data platform that generates custom, photorealistic image datasets with automated pixel-perfect labeling to support training and improving AI and computer vision models; using physics-based rendering and simulation rather than generative AI, it produces high-fidelity synthetic images that mirror real-world conditions and handle diverse scenarios, lighting, camera angles, object motion, and edge cases with controlled precision, which helps eliminate data bias, reduce manual labeling, and dramatically cut data preparation time by up to 90%. Designed to give teams the right data for model training rather than relying on limited real datasets, Symage lets users tailor environments and variables to match specific use cases, ensuring datasets are balanced, scalable, and accurately labeled at every pixel. It is built on decades of expertise in robotics, AI, machine learning, and simulation, offering a way to overcome data scarcity and boost model accuracy.
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Rendered.ai
Overcome challenges in acquiring data for machine learning and AI systems training. Rendered.ai is a PaaS designed for data scientists, engineers, and developers. Generate synthetic datasets for ML/AI training and validation. Experiment with sensor models, scene content, and post-processing effects. Characterize and catalog real and synthetic datasets. Download or move data to your own cloud repositories for processing and training. Power innovation and increase productivity with synthetic data as a capability. Build custom pipelines to model diverse sensors and computer vision inputs. Start quickly with free, customizable Python sample code to model SAR, RGB satellite imagery, and more sensor types. Experiment and iterate with flexible licensing that enables nearly unlimited content generation. Create labeled content rapidly in a hosted, high-performance computing environment. Enable collaboration between data scientists and data engineers with a no-code configuration experience.
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