We earn commission when you buy through affiliate links.
This does not influence our reviews or recommendations.Learn more.
Synthetic data is essential for training machine learning models, testing apps, and gaining business insights.
This is where synthetic data generation tools step in.
These tools can create realistic, privacy-safe, scalable synthetic datasets tailored to your models needs.
it’s possible for you to trust Geekflare
At Geekflare, trust and transparency are paramount.
it’s possible for you to connect MOSTLY AI to your apps or systems through its API.
It works with structured data, including numbers, categories, dates, and geolocation data.
It offers a free plan with limited features; paid plans start at $3 monthly.
MOSTLY AI Key Features
2.
Synthesized.io
Synthesized.io is a generative AI-poweredsyntheticdata generator that creates realistic, production-like test data while protecting sensitive information.
It uses intelligent masking techniques to help developers generate accurate data for testing and development with low compliance risks.
It works with Kubernetes to make database provisioning fast and cloud-friendly.
Synthesized.io uses a Data as Code approach to ensure test data stays up-to-date, compliant, and production-like.
It quickly detects and fixes data quality issues, helping teams address problems before they cause disruptions.
Synthesized.io offers custom pricing tailored to business needs, focusing on enterprise-scale synthetic data solutions and compliance.
It also provides a free developer version with limited features.
Synthesized.io Key Features
3.
YData Key Features
4.
K2View Key Features
5.
It easily integrates with cloud providers, data warehouses, and popular ML tools and frameworks.
For larger needs, a customizable Enterprise Plan is also available.
It can help you protect sensitive information and build datasets for machine learning.
It uses a declarative configuration language that allows you to specify your entire data model as code.
It can import existing data and automatically create detailed, flexible models for your needs.
SDV.dev is ideal for accelerating data-driven projects by providing safe, shareable datasets without compromising sensitive information.
Tofu
Tofu is an open-source Python library for generating synthetic data based on UK biobank data.
The UK Biobank is a research project involving 500,000 middle-aged participants from England, Scotland, and Wales.
Tofu generates random data that matches the structure of UK Biobanks baseline dataset:
10.
Twinify
Twinify is an open-source Python library designed to generate and preserve privacy-preserving synthetic datasets.
It uses advanced differential privacy techniques to generate synthetic data with identical statistical distributions.
It is ideal for researchers, analysts, and developers to analyze data while maintaining compliance with privacy regulations.
Datanamic
Datanamic Data Generator helps you quickly create realistic test data for your databases.
It supports and ensures referential integrity in the generated data.
It generates data based on column characteristics such as email, name, and phone number.
Its ability to anonymize sensitive production data ensures privacy during testing or showcasing.
What Is Synthetic Data?
Synthetic data is artificially generated data that statistically resembles the old dataset.
It can be used with real data to support and improve AI models or as a substitute.
