The importance of data quality & profiling for the success of Machine Learning In today's world, businesses around the globe are generating a vast amount of data. To be able to adopt a data-driven initiative, organizations must manage data...
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In the dynamic landscape of Data-Centric AI, data quality is crucial for the success of any analytics or machine learning initiative. Data profiling is an essential process that provides insights into the intricacies of your datasets,...
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...
Business across all sectors, from retail to banking, rely on relational databases to extract competitive insights. However, due to the privacy regulations set in place to protect individuals’ data, the available information is currently...
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...
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 build their solutions. The landscape of...
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 trading, fraud detection, telecommunications,...
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 – is currently revolutionizing the industry, as...
Synthetic data is a cornerstone of Data Centric-AI, an approach that focuses primarily on data quality rather than models. For the past few years, synthetic data gained attention because of a wide range of applications such as data...
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, diverse, and labeled data can be quite a...
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...
Coined by Andrew Ng in 2021, the concept of “Data-Centric AI” has taken both academia and industry by storm. It has given rise to hundreds of research publications, fostered the creation of special tracks and colloquiums in the most...