Photo by Roman Kraft on Unsplash As machine learning continues to evolve, the use of Large Language Models (LLMs) has become increasingly prevalent, particularly in complex tasks requiring deep understanding and generation of human-like...
Photo by Isaac Smith on Unsplash Synthetic data bootstrap In the dynamic landscape of organizations high-quality data is a requirement for the development of many solutions - from software testing and validation all the way to Artificial...
While allowing for meaningful data behavior, it is crucial that synthetic data safeguards individual privacy. Therefore, ensuring the efficacy of synthetic data applications also requires a strong assessment of re-identification risks....
One of the most valuable and unique characteristics of synthetic data is that it keeps the properties and behavior of original data without a one-to-one link with the real events, thus fostering data privacy and enabling secure data...
A merry data analysis for all As the holiday season approaches, it's not just about decorating trees and sharing gifts; it's also a time to give back to the community and spread joy. This year, why not celebrate the season of giving by...
An Advent to explore Generative AI and Synthetic Data Holidays are approaching and you are feeling like you want to explore something new - synthetic data might just be it! Options are always great, and data profiling is always a good...
The Power of Data Quality, Orchestration, Profiling, and Synthetic Data Retail is not only a fast-paced but also a highly competitive landscape, demanding from the players to be always ahead of the competition. The adoption of AI and...
One of the most important applications of synthetic data is its use in developing machine learning solutions – to train and test machine learning models – when real data is hard to collect or sensitive to share. For that reason, it is...
Synthetic data, designed to mimic real-world datasets, must be able to provide the same answers as real data to be valuable. For instance, when determining the average of customers that buy certain products, the result returned by the...
As organizations increasingly rely on synthetic data to improve their machine learning models, ensuring that the relations like pairwise distributions and correlations are kept in synthetic data is part of the fidelity assessment whenever...
When generating synthetic data, it is key that new data mimics the distribution of the original data to ensure that the synthetic dataset is a realistic representation of real-world data. In that sense, evaluating how the synthetic data...
As Synthetic Data becomes a must-have for the future of AI, guaranteeing its quality becomes indispensable. Fidelity, one of the main pillars of synthetic data evaluation, is crucial in ensuring that synthetic datasets accurately represent...
