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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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 text. Retrieval-Augmented...
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 Intelligence (AI) initiatives. In...
Dive into data management with our latest whitepaper, which presents an in-depth Gap analysis among YData Fabric, Alation, and Informatica—three solutions in the realm of data catalogs. These platforms are chaging how organizations govern,...
In the dynamic landscape of software development, the significance of effective Test Data Management (TDM) cannot be overstated. Traditional approaches, such as IBM InfoSphere Optim, have long been the backbone of this crucial process,...
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,...
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...
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...
Generating synthetic data lays a crucial role in addressing the problematic aspects of data in Data Science, such as balancing classes, expanding small datasets, and securely sharing sensitive information like bank transactions while...
In today's data-driven world, privacy concerns have become paramount. The use of personal data in various applications raises ethical and legal questions, prompting the need for privacy-preserving techniques. Differential privacy has...
Photo by Roman Synkevych on Unsplash A probabilistic approach to fast synthetic data generation with ydata-synthetic To find synthetic data generation within the same sentence as Gaussian Mixture Models (GMMs) sounds odd, but it makes a...
I improved a model to save a hypothetical auto insurance company almost $200 per claim! One of the biggest mistakes that junior data scientists make is focusing too much on model performance while remaining naive about the model’s impact...
The value of data and the adoption of data-driven strategies have proven valuable for many organizations, particularly the financial services sector, with cases ranging from fraud detection to improved credit scoring. Nevertheless, privacy...