Synthetic Data

Synthetic data is artificially generated information that mimics real-world datasets without exposing sensitive information. We analyzed dozens of synthetic data platforms and generation techniques across industries.

Benchmark of Top 3 Synthetic Document Generator

Synthetic DataOct 14

Synthetic document generators create annotated, realistic document images that help train and evaluate machine learning models without relying on large, manually labeled datasets. We benchmark leading synthetic document generators by creating more than 2,500 synthetic documents, comparing their effectiveness in realistic layouts, accurate numerical data, and useful training datasets for document analysis tasks.

Synthetic DataOct 8

Synthetic Data vs Real Data: Benefits, Challenges

Synthetic data is widely used across various domains, including machine learning, deep learning, generative AI (GenAI), large language models, and data analytics. According to Gartner, by 2030, synthetic data use will outweigh real data in AI models.

Synthetic DataSep 26

Top 20+ Synthetic Data Use Cases

Synthetic data is gaining widespread popularity and applicability across industries, including machine learning, deep learning, and generative AI (GenAI). Synthetic data offers solutions to challenges such as data privacy concerns and limited dataset sizes. It is estimated that synthetic data will be preferred over real data in AI models by 2030.

Synthetic DataSep 24

Synthetic Data Generation Benchmark & Best Practices

We benchmarked 7 publicly available synthetic data generators sourced from 4 distinct providers, utilizing a holdout dataset comprising 70,000 samples, with 4 numerical and 7 categorical features, to evaluate their performance in replicating real-world data characteristics. Below, you can see the benchmark results where we statistically compare the synthetic data generators.

Synthetic DataSep 17

Synthetic Data Chatbot: Top 26 Tools to Test and Train Them

Synthetic data is expected to surpass real-world data as the primary source for AI training by 2030, and chatbots are no exception. Once mainly used to train bots when real conversations were scarce or sensitive, it’s now just as vital for testing, validating performance, stress-testing, and ensuring compliance when real logs aren’t safe or available.

Synthetic DataSep 4

Synthetic Users Explained: Top 7 AI User Research Tools

Traditional research requires weeks of finding participants, scheduling interviews, and analyzing results manually. Synthetic user platforms enable teams to create thousands of realistic user profiles instantly, allowing them to test ideas, messaging, and user flows.

Synthetic DataAug 20

Top 5 Synthetic Data Finance Applications

In my eleven years of academic and professional experience, I observe that artificial intelligence has a diverse set of applications in financial services from process automation to chatbots and fraud detection.

Synthetic DataApr 10

12+ Data Augmentation Techniques for Data-Efficient ML

Data augmentation techniques generate different versions of a real dataset artificially to increase its size. Computer vision and natural language processing (NLP) models use data augmentation strategy to handle with data scarcity and insufficient data diversity. Data-centric AI/ML development practices such as data augmentation can increase accuracy of machine learning models.