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...
High-dimensional datasets are at the heart of many business applications and domains, from financial services to telecommunications, retail, and healthcare. These datasets, characterized by a large number of columns — sometimes hundreds or...
Attending to the current panorama of privacy regulations such as GRPD and CCPA, synthetic data has become an indispensable strategy for organizations looking to unlock their data sharing and development initiatives. Synthetic data is...
Photo by Nemesia Production on Unsplash In the current Data-Centric AI paradigm, where all businesses seek to leverage the power of their data for any competitive advantage they can get, organizations face a critical choice: to buy or...
Cover Photo by Agence Olloweb on Unsplash In the fast pace of today’s data-driven world, synthetic data is becoming an important resource of data projects across industries. Automated decision-making systems in healthcare, algorithmic...
Cover Photo by Philipp Katzenberger on Unsplash In the last decade, we’ve increasingly focused on model-centric Artificial Intelligence, building ever more flexible machine learning models. However, a new paradigm shift – Data-Centric AI –...
Cover Photo by James Harrison on Unsplash In the rapidly evolving Artificial Intelligence landscape, data quality is the lifeblood that fuels the development of accurate and efficient models. However, accessing and acquiring high-quality,...
Since Andrew Ng coined the term in 2021, the number of companies that identify themselves as providing data-centric AI tools has exploded. From synthetic data to data monitoring, companies all over the machine learning workflow have jumped...
