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.
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 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.
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.
Top 20+ Synthetic Data Use Cases
Synthetic data offers solutions to challenges such as data privacy concerns and limited dataset sizes. Synthetic data is gaining widespread popularity and applicability across industries, including machine learning, deep learning, and generative AI (GenAI). It is estimated that synthetic data will be preferred over real data in AI models by 2030.
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.
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.